Intelligent rail green wave driving method and system based on multi-system linkage

Abstract

The invention provides an intelligent rail green wave driving method and system based on multi-system linkage, relates to the technical field of intelligent rail traffic control, and solves the problem that the overall operation efficiency of a novel rail vehicle under shared right of way is limited. The method comprises the following steps: collecting real-time position and speed data of the intelligent rail vehicle, verifying the data by a station monitoring system, calculating the predicted intersection arrival time of the intelligent rail vehicle, and sending the predicted intersection arrival time to a traffic signal lamp management system; and the predicted intersection arrival time, the current corresponding signal lamp state and intersection traffic flow data are combined and uploaded to a total traffic light control center, and the total traffic light control center determines whether to trigger a green light acceleration operation or not through an authority grading judgment mode, calculates the green light acceleration duration during triggering, generates a green light acceleration instruction and issues the green light acceleration instruction to the corresponding traffic signal lamp. On the premise of guaranteeing the traffic safety of the intelligent rail, social vehicles and pedestrians, the traffic efficiency of the intelligent rail intersection is remarkably improved, the operation and maintenance cost is reduced, and the urban traffic operation order is optimized.

Description

Technical Field

[0001] This invention relates to the field of intelligent rail transit control technology, specifically to an intelligent rail transit green wave driving method and system based on multi-system linkage. Background Technology

[0002] With the continuous evolution of urban public transportation systems, a new type of rail transit is gradually becoming an important part of urban passenger transport, which can be called intelligent rail transit (IRT). This mode of transport, with its environmentally friendly, efficient, and flexible characteristics, undertakes a considerable passenger transport task on specific urban lines. However, in its actual commercial operation, especially on sections where it needs to share road rights with other vehicles, its traffic efficiency and system reliability face a series of technical problems that urgently need to be solved.

[0003] Traditional traffic signal control typically employs preset fixed-cycle patterns, often leaving rail vehicles in a passive waiting state when passing through intersections. This waiting significantly increases travel time, especially during peak traffic hours, making it difficult to achieve ideal overall line travel speeds. While existing technologies attempt to provide green wave traffic coordination for motor vehicles or regular buses, these solutions lack specific design considerations for the unique operating characteristics of these rail vehicles. Their significant vehicle length and relatively stable operating speed range make it difficult to directly adapt existing coordination logic, limiting practical application and failing to fully exploit their traffic potential.

[0004] Existing control systems also have significant shortcomings in information transmission performance. Most collaborative control schemes rely on single wireless communication technologies or wired networks for data transmission, and their inherent transmission delays often fail to meet the stringent requirements of real-time, precise control. Furthermore, in complex environments such as densely built-up urban areas or underground passages, communication links are susceptible to interference, potentially leading to decreased vehicle positioning accuracy or loss of critical control commands. This lack of transmission reliability directly increases the risk of collaborative control failure and affects the overall stability of the system.

[0005] Currently, signal control mechanisms prioritizing traffic flow suffer from several limitations. Many technical solutions adjust traffic light phases in a fixed or pre-programmed manner, lacking dynamic response capabilities based on real-time operational status. The decision on green light duration relies heavily on historical experience, failing to perform intelligent calculations and real-time adaptation based on multi-dimensional information such as vehicle instantaneous position and speed. While this control method can provide some convenience, its efficiency improvement is limited, failing to achieve optimal dynamic allocation of traffic resources.

[0006] In complex intersection environments with multiple traffic participants, balancing the right-of-way demands of different stakeholders becomes another challenge. Existing collaborative systems generally lack a refined and quantifiable hierarchical management mechanism for traffic rights. Granting priority to rail vehicles can easily conflict with the normal traffic needs of other vehicles and pedestrians, potentially leading to localized traffic chaos or even safety hazards. This hinders the large-scale application of priority strategies.

[0007] From an implementation and operation perspective, the deployment costs of some existing technologies are high and their system compatibility is poor. To achieve interconnected functions, large-scale modifications to road infrastructure or replacement of core equipment are often required, resulting in huge investment in upgrading a single route. The integration and adaptation process of these systems with existing vehicle control units and urban traffic management platforms is complex and time-consuming, further increasing the difficulty and cost of technology implementation.

[0008] Therefore, the operation of this new type of rail vehicle under the current shared right-of-way system, especially in intersection priority scenarios, faces multiple technical bottlenecks, including limited traffic efficiency, high transmission latency, unintelligent control, right-of-way conflicts, and high deployment costs. The industry urgently needs a collaborative control solution that can be specifically adapted to its operational characteristics, possesses high-speed and reliable communication capabilities, enables intelligent quantitative dynamic control, and has good system compatibility and economy. Summary of the Invention

[0009] The purpose of this invention is to address the limitations in overall operational efficiency of new rail vehicles operating under shared right-of-way conditions, caused by low intersection traffic efficiency, insufficient control information transmission delay and reliability, rigid signal control mechanisms, conflicts of authority among multiple traffic entities, and high system modification costs. Therefore, this invention proposes a smart rail green wave operation method and system based on multi-system linkage. This invention can significantly improve the intersection traffic efficiency of smart rail, reduce operation and maintenance costs, and optimize urban traffic order while ensuring the safety of smart rail, other vehicles, and pedestrians.

[0010] The present invention employs the following technical solutions to achieve its objective: A smart rail transit green wave operation method based on multi-system linkage, the method includes the following steps: S1. Collect real-time location and speed data of the intelligent rail vehicle through the intelligent rail vehicle-mounted unit, and upload it to the station monitoring system through the wireless communication network; S2. The station monitoring system verifies the received real-time location and speed data, and determines whether the intelligent rail vehicle has entered the intersection warning range based on the real-time location and the preset intersection location information; when it is determined that the vehicle has entered the intersection, it calculates the estimated arrival time of the intelligent rail vehicle at the intersection and sends the data containing the estimated arrival time at the intersection to the traffic signal management system. S3. The traffic signal management system forwards the data containing the estimated arrival time at the intersection to the central traffic light control center, and uploads the current corresponding signal light status and intersection traffic flow data. S4. Based on the estimated arrival time at the intersection, the status of the traffic lights, and the traffic flow data at the intersection, the central traffic light control center determines whether to trigger a green light acceleration operation through a hierarchical permission judgment method. When triggered, the green light acceleration duration is calculated, a green light acceleration command is generated, and sent to the corresponding traffic light.

[0011] Specifically, in step S1, the real-time position and speed data of the intelligent rail vehicle are collected by: using a multi-mode high-precision positioning module corresponding to Beidou and GPS to obtain the real-time position and speed data at a preset sampling frequency, and simultaneously performing real-time compensation through an inertial navigation compensation module. The wireless communication network is a 5G communication network; when the signal of the 5G communication network does not meet the preset requirements, the output data of the inertial navigation compensation module is activated for positioning compensation.

[0012] Furthermore, in step S2, the estimated arrival time of the intelligent rail vehicle at the intersection is calculated. As shown in the following formula:

[0013] In the formula, The preset threshold for the intersection warning range; This represents the real-time distance between the current location of the intelligent rail vehicle and the intersection. This refers to the real-time speed of the intelligent rail transit vehicle. This is a speed correction factor set according to traffic hours; This represents the total latency of system data transmission and processing.

[0014] Furthermore, in step S4, the permission level determination specifically involves: obtaining a preset intelligent rail transit vehicle priority score. Priority score for social vehicles and pedestrian priority score Calculate the weighted sum As shown in the following formula:

[0015] In the formula, , , These are the dynamic weighting coefficients corresponding to intelligent rail vehicles, private vehicles, and pedestrians, respectively; based on the weighted sum... The calculation definition, in , , Under the preset dynamic changes, only when The green light acceleration operation is only triggered when the value exceeds the preset threshold. When triggered, calculate the green light acceleration duration. As shown in the following formula:

[0016] In the formula, This represents the remaining duration of the current red light. The preset green light duration is set; simultaneously, the calculated green light acceleration time is verified. Whether the traffic light is within the preset acceleration threshold range is determined; a green light acceleration command is only generated and issued upon successful verification. When the green light acceleration command is issued, the traffic lights at the intersection are switched from the current red light state to an earlier state. It turns green after a certain time and remains green for at least the necessary time for the smart rail to completely pass through the intersection. Plus Duration.

[0017] Preferably, between step S2 and step S3, the method further includes: if the station monitoring system detects an interruption in the connected communication network link before sending data to the traffic signal management system, it temporarily stores the data in the data cache module and resends the data after the communication network link is restored; The network architecture for data transmission in this method includes: a 5G wireless network for communication between the intelligent rail vehicle unit and the station monitoring system; an optical fiber network for sequentially connecting and communicating with the station monitoring system, the traffic signal management system and the main traffic light control center; and a 4G LTE backup communication link that is automatically switched on when the optical fiber network is interrupted.

[0018] This invention also provides a smart rail transit green wave system for implementing the aforementioned method, the system comprising the following components: The intelligent rail vehicle unit is deployed on the intelligent rail vehicle to collect the real-time location and speed data of the intelligent rail vehicle and upload it to the station monitoring system through a wireless communication network. The station monitoring system is deployed along the intelligent rail line to verify the received real-time location and speed data, and based on the real-time location and preset intersection location information, to determine whether the intelligent rail vehicle has entered the intersection warning range. When it is determined that the vehicle has entered the intersection, the system calculates the estimated arrival time of the intelligent rail vehicle at the intersection and sends the data containing the estimated arrival time to the traffic signal management system. The traffic signal management system is deployed at traffic intersections to forward data containing the estimated arrival time at the intersection to the central traffic light control center, and to upload the current traffic light status and intersection traffic flow data. The central traffic light control center is used to determine whether to trigger a green light acceleration operation based on the estimated arrival time at the intersection, the status of the traffic lights, and the traffic flow data at the intersection, through a hierarchical judgment method. When triggered, it calculates the green light acceleration duration, generates a green light acceleration command, and sends it to the corresponding traffic light.

[0019] Preferably, the intelligent rail vehicle unit includes: a multi-mode high-precision positioning module, an inertial navigation compensation module, and a 5G-V2X communication module; The multi-mode high-precision positioning module is used to collect the real-time position and velocity data using a preset sampling frequency; the inertial navigation compensation module is used to compensate the output of the multi-mode high-precision positioning module when the signal of the wireless communication network does not meet the preset requirements, so as to maintain the continuity of the real-time position and velocity data.

[0020] Preferably, the station monitoring system includes: an intersection location database and a data caching module; The intersection location database is used to store the coordinate information of each traffic intersection along the intelligent rail line and the corresponding intersection warning range threshold; the data caching module is used to temporarily store communication data within a preset time when the communication network link between the station monitoring system and the traffic signal management system is interrupted, and to resend the data after the communication network link is restored.

[0021] Preferably, the main traffic light control center includes: an edge computing module and a core algorithm module; The edge computing module is used to perform data verification, permission classification determination, and instruction generation operations; the core algorithm module has built-in quantitative formulas for calculating the estimated arrival time at the intersection, the green light acceleration time, and performing the permission classification determination.

[0022] Preferably, the system further includes a dual transmission network architecture, consisting of a 5G wireless network, an optical fiber network, and a 4G LTE backup communication link; the 5G wireless network is used for data transmission between the intelligent rail vehicle unit and the station monitoring system; the optical fiber network is used for data transmission between the station monitoring system, the traffic signal management system, and the main traffic light control center; and the 4G LTE backup communication link is used to automatically start when the optical fiber network is detected to be interrupted for more than a preset time threshold.

[0023] In summary, due to the adoption of this technical solution, the beneficial effects of this invention are as follows: In terms of improving the traffic efficiency of intelligent rail transit vehicles, this invention effectively reduces the passive waiting time of intelligent rail transit vehicles at intersections through multi-system collaborative linkage and dynamic signal control. As a result, the travel speed and punctuality rate of intelligent rail transit lines are significantly improved, and vehicles can pass through traffic intersections more smoothly, thereby optimizing the overall operational efficiency of the lines.

[0024] This invention substantially enhances the reliability and stability of the intelligent rail transit system. Through the constructed dual transmission network and positioning compensation mechanism, it ensures continuous, accurate, and low-latency transmission of critical control data in complex environments. The overall system failure rate and the probability of linkage failure are controlled at extremely low levels, and positioning accuracy is maintained over a long period, laying the foundation for continuous and reliable control.

[0025] This invention ensures priority passage for intelligent rail transit while effectively maintaining overall traffic order. By introducing a quantified, hierarchical permission determination mechanism, it scientifically balances the right-of-way needs of various traffic participants, including intelligent rail transit vehicles, other vehicles, and pedestrians. This makes the allocation of right-of-way more reasonable, significantly reduces the risk of traffic disorder caused by priority conflicts, and promotes the improvement of overall intersection capacity.

[0026] The overall design of this invention achieves high compatibility with existing intelligent rail transit vehicle control systems and traffic management platforms, eliminating the need for large-scale modifications to road infrastructure or replacement of core equipment, thus significantly reducing system deployment difficulty and integration costs. Simultaneously, dynamic signal control based on real-time information reduces energy consumption, optimizing costs from both construction and operation perspectives. Attached Figure Description

[0027] The present invention is described in detail with reference to the following figures, which include three figures as follows: Figure 1 This is a schematic diagram illustrating the overall process of the intelligent rail transit green wave driving method of the present invention; Figure 2 This is a schematic block diagram of a preferred multi-level process control method of the present invention; Figure 3 This is a schematic diagram of the overall architecture of the intelligent rail green wave driving system of the present invention. Detailed Implementation

[0028] 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0030] Example 1 A method for intelligent rail transit green wave operation based on multi-system linkage is described below. A brief overview of the overall process of this method can be found here. Figure 1 The illustration shows the key steps of this method, which can be summarized as follows: S1. Collect real-time location and speed data of the intelligent rail vehicle through the intelligent rail vehicle-mounted unit, and upload it to the station monitoring system through the wireless communication network; S2. The station monitoring system verifies the received real-time location and speed data, and determines whether the intelligent rail vehicle has entered the intersection warning range based on the real-time location and the preset intersection location information. When it is determined that the vehicle has entered the intersection, the system calculates the estimated arrival time of the intelligent rail vehicle at the intersection and sends the data containing the estimated arrival time to the traffic signal management system. S3. The traffic signal management system will forward data containing the estimated arrival time at the intersection to the central traffic light control center, and upload the current status of the corresponding traffic lights and traffic flow data at the intersection. S4. The main traffic light control center determines whether to trigger a green light acceleration operation based on the estimated arrival time at the intersection, the status of the traffic lights, and the traffic flow data at the intersection, through a hierarchical judgment method. When triggered, the green light acceleration duration is calculated, a green light acceleration command is generated, and sent to the corresponding traffic light.

[0031] This embodiment will provide a detailed explanation of the above method steps and preferred methods, and you can also refer to... Figure 2 The specific control flow diagram is shown, allowing for a simultaneous understanding of the key aspects of each step in the method of this embodiment.

[0032] The method in this embodiment starts from the intelligent rail vehicle unit deployed on the intelligent rail train, which integrates a multi-mode high-precision positioning module, an inertial navigation compensation module, and a 5G-V2X communication module. Among them, the multi-mode high-precision positioning module simultaneously receives signals from the Beidou satellite navigation system and the Global Positioning System (GPS), with a preferred sampling frequency of 10Hz, thereby collecting data such as the intelligent rail vehicle's precise geographical location, driving speed, and braking status in real time. In terms of performance indicators, its positioning accuracy is constrained, and it is recommended to control it to no more than 5cm.

[0033] The real-time generated position and speed data are continuously uploaded to the nearest pre-configured station monitoring system along the line through the 5G-V2X communication module via the low-latency 5G wireless communication network at a frequency of once per second. In addition, in actual operation, it is inevitable to encounter environments where the 5G signal is temporarily weakened or lost, such as tunnels and dense building clusters. Therefore, as a preference of this embodiment, in order to ensure the continuity and reliability of key positioning data, a redundancy compensation mechanism is designed: when the signal strength or quality of the 5G communication network does not meet the preset communication requirements, the inertial navigation compensation module in the vehicle-mounted unit will be activated; it uses sensors such as gyroscopes and accelerometers to calculate the displacement and attitude changes of the intelligent rail vehicle during the short interval when the signal is temporarily invalid, and performs real-time compensation on the output of the multi-mode high-precision positioning module, so as to maintain the continuous output of the position and speed data streams. The positioning deviation during the compensation period is also restricted to a relatively low level, effectively avoiding data breakpoints caused by signal occlusion.

[0034] The station monitoring system is the key intermediate node and the first-level data processing link of the method in this embodiment, and is deployed in each station along the intelligent rail line. The system continuously listens for and receives the data packets uplinked from the intelligent rail vehicle-mounted unit of passing intelligent rail trains through the 5G network. After receiving the real-time position and speed data, the system does not directly adopt it, but first performs data verification; the core standard of this verification is to evaluate the credibility of the positioning data. In this embodiment, it is preferably to judge whether the deviation between the position information contained in the data and its theoretical calculated value or historical trajectory is within an acceptable threshold, and the positioning data with a deviation less than a certain value is determined to be valid data. For the invalid data that fails to pass the verification, the system will activate the retransmission mechanism and request retransmission from the corresponding intelligent rail vehicle-mounted unit once every 100 ms, repeating at most 3 times, so as to obtain accurate information as much as possible within its interval; the valid data that has passed the verification is sent to the subsequent decision logic.

[0035] In this embodiment, a road intersection position database is pre-stored inside the station monitoring system. This database stores the longitude and latitude coordinate information of the traffic intersections along the intelligent rail line, as well as the warning range thresholds personalized for each intersection. It is preferably divided into main line intersections and branch line intersections. The corresponding threshold for the main line intersection is 800 meters, and for the branch line it is 500 meters. The system compares the real-time position of the current intelligent rail vehicle with the intersection coordinates in the database and calculates its distance from the nearest intersection ahead; when the system determines that this distance is less than the warning range threshold of this intersection, that is, it is determined that the intelligent rail vehicle has entered the intersection warning range. For example, when it has entered the interval 800 meters away from the main line intersection, it will trigger the linkage preparation.

[0036] At this point, the station monitoring system predicts the arrival time of the intelligent rail vehicle at the intersection. This prediction requires calculation using a quantitative formula. Ideally, this formula should be maintained by the core algorithm module of the central traffic light control center, allowing it to fine-tune relevant parameters based on the overall urban traffic signal situation and provide access to the station monitoring system when needed; alternatively, it can be directly preset and stored within the station monitoring system. In this embodiment, the estimated arrival time of the intelligent rail vehicle at the intersection is calculated. As shown in the following formula:

[0037] In the formula, This represents the preset warning range threshold for this intersection; This represents the real-time distance between the current location of the intelligent rail vehicle and the intersection. The real-time speed of the intelligent rail vehicle is represented by the value of 25-35 km / h for the intelligent rail vehicle object in this embodiment. It is a speed correction coefficient that is dynamically selected based on the current traffic flow and time period. Taking into account deceleration before intersections, this coefficient can be determined as 0.9 for peak hours, 1.0 for off-peak hours, and 1.1 for nighttime hours, thus reflecting the difference between the actual average speed and the theoretical speed. This is the total delay time reserved for data transmission, processing, and other processes within the system.

[0038] Calculate the estimated time to reach the intersection Subsequently, the station monitoring system generates a structured linkage request data packet, which includes the intelligent rail vehicle's identity information and estimated arrival time at the intersection. The system transmits requests to the traffic signal management system near the target intersection via a fiber optic network. As a preferred embodiment, considering the possibility of momentary interruptions in communication with the traffic signal management system, the station monitoring system is also independently equipped with a data caching module, preferably with a capacity of at least 16GB, to continuously cache all pending data for a certain period. Once the system detects an interruption in the fiber optic network link with the traffic signal management system, it automatically stores the linkage request and other data temporarily in this caching module; after the network link is restored, the system immediately reads the data from the cache and resends it, thus ensuring that critical instructions are not lost.

[0039] In this embodiment, the traffic signal management system is deployed at specific traffic intersections and is responsible for receiving linkage requests from the upstream station monitoring system. Before forwarding the request to the central traffic light control center, the system performs a secondary verification. This verification aims to prevent false triggering across intersections due to location drift or system misjudgment. Specifically, the system compares the location information of the intelligent rail vehicle or the target intersection identifier carried in the linkage request with the precise coordinates of the intersection actually managed by the system to ensure that the request targets the correct intersection, thereby filtering out invalid or incorrect linkage requests. After verification, the traffic signal management system forwards the linkage request to the higher-level central traffic light control center, which is responsible for urban area coordination, via a dedicated communication link, which is also part of the fiber optic network.

[0040] Simultaneously, the traffic signal management system also collects and uploads two types of real-time status information: first, the current traffic light status at the intersection, including the red, green, and yellow light colors for each direction and the remaining countdown time; second, real-time traffic flow data at the intersection, provided by detection loops, cameras, or radar equipment, including the instantaneous flow of vehicles in each lane and the number of pedestrian crossing requests or numbers. This real-time data provides comprehensive information support for subsequent intelligent decision-making at the central traffic light control center.

[0041] The central traffic light control center is the decision-making layer of the method in this embodiment. After receiving the forwarded data packet from the traffic signal management system, it begins the final analysis and command. This control center integrates an edge computing module and a core algorithm module to ensure extremely low computational latency. The decision-making process begins with a multi-factor fusion-based permission hierarchy determination, determined by a quantitative permission determination formula built into the core algorithm module of the control center, as follows:

[0042] In the formula, The priority score for the pre-set intelligent rail transit vehicles; The score is the preset priority score for social vehicles; Score the pedestrian priority based on the preset criteria; This is a weighted sum of the three factors. This formula defines the overall traffic priority of intelligent rail vehicles relative to other vehicles and pedestrians at the current moment. This reflects its priority as public transportation. It can be preset according to time periods based on real-time traffic flow data at intersections. To be determined based on pedestrian crossing needs; , , These are three adjustable weighting coefficients, which are set differently according to different times of day, including morning peak, evening peak, off-peak, and nighttime, reflecting the different focuses of traffic management strategies at different times. During peak hours, the weighting of private vehicles can be appropriately increased. To balance overall traffic flow, the weight of intelligent rail transit may be increased during off-peak hours. To ensure its punctuality rate.

[0043] As a preferred embodiment, the priority score of the intelligent rail transit vehicle is... The priority score for pedestrians is fixed at 60 points, which is the first-level priority; the priority score for social vehicles is fixed at 40 points during peak hours and 20 points during off-peak hours, which is the second-level priority; and the priority score for pedestrians is fixed at 30 points, which is the third-level priority. , , satisfy Constraints, during peak hours, , , During off-peak hours, , , During nighttime hours, , , .

[0044] The central traffic light control center is using weighted sums The calculation check is performed under the definitional constraints if and only if The green light acceleration operation is triggered only at specific times, while also taking into account the estimated arrival time of the intelligent rail vehicle. The current status of the traffic lights (whether it is red and the remaining red / green light duration), and a pre-stored time required for the intelligent rail system to completely pass through the intersection. The system comprehensively determines whether to perform a green light acceleration operation; in this embodiment, the example intelligent rail vehicle has a body length of 61 meters, and when it is running at a speed of 30 km / h, That is, it is fixed at 7.32 seconds.

[0045] When the green light acceleration operation is triggered and executed, the central traffic light control center calculates how much earlier the green light should be turned on, i.e., how much the current red light should be shortened, and records this as the green light acceleration time. The core formula used is as follows:

[0046] In the formula, This represents the remaining duration of the current red light. The preset green light duration is set; simultaneously, the calculated green light acceleration time is verified. Whether the traffic light is within the preset acceleration threshold range is determined; a green light acceleration command is only generated and issued upon successful verification. When the green light acceleration command is issued, the traffic lights at the intersection are switched from the current red light state to an earlier state. It turns green after a certain time and remains green for at least the necessary time for the smart rail to completely pass through the intersection. Plus The duration. In this embodiment, the calculated... Should meet Seconds, because excessively shortening red lights could cause excessive congestion for vehicles in other directions; and if If the value is negative, the red light should immediately turn green. Subsequently, the central traffic light control center generates a value containing specific... The green light acceleration command is quickly transmitted to the requesting traffic signal management system via fiber optic network link.

[0047] Upon receiving instructions from the central traffic light control center, the traffic signal management system immediately activates the intersection's signal controllers to perform phase switching. By shortening the current red light duration or activating the green light phase earlier, it ensures that the intelligent rail transit train encounters a green light a certain time before arriving at the intersection, thus enabling non-stop passage. As a complementary measure, at the moment of signal switching, the system can simultaneously trigger the electronic display screens at the intersection to display information such as "Intelligent Rail Transit Priority" to inform vehicles and pedestrians, ensuring traffic safety and efficiency.

[0048] As a preferred supplement to this embodiment, after the command is executed, the traffic signal management system will send the new status of the traffic lights after the actual switching as feedback to the main traffic light control center within a very short time. Upon receiving the feedback, the control center will evaluate the effectiveness of this decision and execution based on the subsequent data on the actual passage of the intelligent rail at the intersection. By comparing the actual passage time of the intelligent rail with the theoretical green light start time, if it is found that the green light does not turn on in advance or even remains red when the intelligent rail passes, and the error exceeds the allowable calculation range, it indicates that there is a deviation in prediction or control. At this time, the control center activates its closed-loop optimization mechanism to automatically fine-tune the weight coefficient of the next similar intersection in the permission determination formula. Through continuous self-learning and parameter optimization, the control accuracy and adaptability of the entire method execution process are continuously improved, forming a complete intelligent control process, enabling intelligent rail vehicles to achieve green wave operation.

[0049] Example 2 Based on Example 1, this example provides a smart rail green wave driving system that implements the method of Example 1. Figure 3 The system's composition and connections are shown and can be viewed synchronously; the system includes: The intelligent rail vehicle unit is deployed on the intelligent rail vehicle to collect the real-time location and speed data of the intelligent rail vehicle and upload it to the station monitoring system through the wireless communication network. The station monitoring system is deployed along the intelligent rail line to verify the received real-time location and speed data. Based on the real-time location and the preset intersection location information, it determines whether the intelligent rail vehicle has entered the intersection warning range. When it is determined that the vehicle has entered the intersection, it calculates the estimated arrival time of the intelligent rail vehicle at the intersection and sends the data containing the estimated arrival time to the traffic signal management system. The traffic signal management system is deployed at traffic intersections to forward data including estimated arrival times at the intersection to the central traffic light control center, and to upload the current status of the corresponding traffic lights and traffic flow data at the intersection. The central traffic light control center is used to determine whether to trigger a green light acceleration operation based on the estimated arrival time at the intersection, the status of the traffic lights, and the traffic flow data at the intersection, through a hierarchical judgment of permissions. When triggered, it calculates the green light acceleration duration, generates a green light acceleration command, and sends it to the corresponding traffic light.

[0050] In this embodiment, the intelligent rail transit vehicle unit includes a multi-mode high-precision positioning module, an inertial navigation compensation module, and a 5G-V2X communication module. The multi-mode high-precision positioning module is used to collect real-time position and speed data using a preset sampling frequency; the inertial navigation compensation module is used to compensate for the output of the multi-mode high-precision positioning module when the signal of the wireless communication network does not meet the preset requirements, thereby maintaining the continuity of real-time position and speed data.

[0051] In this embodiment, the station monitoring system includes an intersection location database and a data caching module. The intersection location database stores the coordinate information of each traffic intersection along the intelligent rail line and the corresponding intersection warning range threshold. The data caching module temporarily stores communication data for a preset duration when the communication network link between the station monitoring system and the traffic signal management system is interrupted, and retransmits the data after the communication network link is restored.

[0052] In this embodiment, the main traffic light control center includes an edge computing module and a core algorithm module. The edge computing module is used to perform data verification, permission level determination, and instruction generation calculations; the core algorithm module has built-in quantitative formulas for calculating the estimated arrival time at the intersection, the green light acceleration time, and performing permission level determination.

[0053] As a preferred embodiment, the system also includes a dual transmission network architecture, consisting of a 5G wireless network, an optical fiber network, and a 4G LTE backup communication link; the 5G wireless network is used for data transmission between the intelligent rail vehicle unit and the station monitoring system; the optical fiber network is used for data transmission between the station monitoring system, the traffic signal management system, and the main traffic light control center; and the 4G LTE backup communication link is used to automatically start when an optical fiber network interruption is detected for more than a preset time threshold.

[0054] Example 3 Based on Example 1, this example demonstrates the effect of the method when it is executed using a more specific example.

[0055] I. System Deployment, Taking Yibin Smart Rail T1 Line as an Example Deployment of onboard units for the intelligent rail transit system: 5G-V2X communication modules (Huawei ME9000s, supporting the NRn78 frequency band) are installed on all operating vehicles on the Yibin Intelligent Rail Transit T1 line; a BeiDou + GPS multi-mode high-precision positioning module with a positioning accuracy of ±3cm and a sampling frequency of 10Hz is also installed; and an inertial navigation compensation module is also installed. The onboard units are linked with the intelligent rail transit vehicle control system to synchronously acquire vehicle speed and braking status data.

[0056] Station monitoring system deployment: Monitoring servers, model Advantech IPC-610L, are deployed at 14 stations along the Yibin Smart Rail T1 line. They have a built-in intersection location database that stores the latitude and longitude coordinates of 25 shared right-of-way intersections on the T1 line. The warning range for main line intersections is 800 meters and for branch line intersections is 500 meters. The system also includes a 16GB data cache module. The server receives data from the on-board unit through the Yibin Mobile 5G base station, and the performance constraint for response time is less than or equal to 5ms.

[0057] Traffic signal management system deployment: Management terminals are deployed near each traffic intersection along the T1 line, connected to the station monitoring system and the main traffic light control center via fiber optic cable, supporting command reception and signal light status feedback; and it is compatible with the control protocol of the existing Kehua KC-SG-4C signal lights, so there is no need to replace the signal light equipment.

[0058] The overall traffic light control center is deployed at the Yibin City Traffic Command Center, integrating an Intel Xeon Gold 6330 edge computing server and a database server. It stores the traffic light cycle data for Line T1, with a peak cycle of 120 seconds, an off-peak cycle of 90 seconds, and a nighttime cycle of 60 seconds. It also stores real-time traffic flow data at intersections and intelligent rail transit operation parameters. The intelligent rail transit departure interval is 10 minutes during peak hours, and the train body length is 61 meters. The edge computing module has a built-in core quantization algorithm with a rolling optimization cycle of 15 minutes.

[0059] II. Operational Example: Taking Yibin Smart Rail T1 Line at Xufu Road Taking the Rongzhou Road intersection as an example At 7:30 AM during the morning rush hour, the intelligent rail transit vehicle departed from New Century Square Station and headed towards Jiudu Road Station, continuing towards Yangtze River Bridge South Station before reaching Xufu Road. At a distance of 700 meters from the intersection of Rongzhou Road, the vehicle-mounted unit obtains the latitude and longitude coordinates (104.63°E, 28.76°N) through a high-precision positioning module, and the real-time driving speed. The speed is 30 km / h.

[0060] The onboard unit uploads data to the Yangtze River Bridge South Station monitoring system via the 5G network. The system completes the first-level verification (positioning deviation 1.2cm, valid); compares it with the intersection location database, confirms that the vehicle has entered the 800-meter warning range, and calculates the estimated arrival time at the intersection. It is approximately 13.35 seconds.

[0061] The station monitoring system will transmit the linkage request to the traffic light management system at the intersection via fiber optic network. After completing the secondary verification, the system will report that the current light status is red. The time is 50 seconds, and the real-time traffic flow at the intersection is 25 vehicles per lane and 5 pedestrians per lane.

[0062] The central traffic light control center completes three levels of verification (vehicles are within the scheduled shifts) and determines traffic priority (morning rush hour). , (30 points, meeting the trigger condition); calculate the green light acceleration time. The time is 39.33s. A green light acceleration command is generated: 50s-39.33s=10.67s later the red light turns green. The green light lasts for at least 10 seconds to allow the intelligent rail vehicle to pass through completely.

[0063] After receiving the instruction, the traffic signal management system controls the red light at the intersection to start a countdown of 10.67 seconds, then switches to green, and the electronic screen at the intersection displays "Intelligent Rail Transit Priority Passage"; within 100ms, it transmits the actual light status back to the central traffic light control center.

[0064] The intelligent rail vehicle arrived at the intersection 13.35 seconds later than scheduled, by which time the green light had already turned on, and the vehicle passed smoothly. The overall traffic light control center assessed that the error in the execution effect was within the threshold, and no adjustment of the weighting coefficient was required.

Claims

1. A smart rail transit green wave operation method based on multi-system linkage, characterized in that, The method includes the following steps: S1. Collect real-time location and speed data of the intelligent rail vehicle through the intelligent rail vehicle-mounted unit, and upload it to the station monitoring system through the wireless communication network; S2. The station monitoring system verifies the received real-time location and speed data, and determines whether the intelligent rail vehicle has entered the intersection warning range based on the real-time location and the preset intersection location information; when it is determined that the vehicle has entered the intersection, it calculates the estimated arrival time of the intelligent rail vehicle at the intersection and sends the data containing the estimated arrival time at the intersection to the traffic signal management system. S3. The traffic signal management system forwards the data containing the estimated arrival time at the intersection to the central traffic light control center, and uploads the current corresponding signal light status and intersection traffic flow data. S4. Based on the estimated arrival time at the intersection, the status of the traffic lights, and the traffic flow data at the intersection, the central traffic light control center determines whether to trigger a green light acceleration operation through a hierarchical permission judgment method. When triggered, the green light acceleration duration is calculated, a green light acceleration command is generated, and sent to the corresponding traffic light.

2. The intelligent rail transit green wave method according to claim 1, characterized in that, In step S1, the collection of real-time position and speed data of the intelligent rail vehicle specifically involves: acquiring the real-time position and speed data at a preset sampling frequency through a multi-mode high-precision positioning module corresponding to Beidou and GPS, and simultaneously performing real-time compensation through an inertial navigation compensation module. The wireless communication network is a 5G communication network; when the signal of the 5G communication network does not meet the preset requirements, the output data of the inertial navigation compensation module is activated for positioning compensation.

3. The intelligent rail transit green wave driving method according to claim 1, characterized in that, In step S2, the estimated arrival time of the intelligent rail vehicle at the intersection is calculated. As shown in the following formula: In the formula, The preset threshold for the intersection warning range; This represents the real-time distance between the current location of the intelligent rail vehicle and the intersection. This refers to the real-time speed of the intelligent rail transit vehicle. This is a speed correction factor set according to traffic hours; This represents the total latency of system data transmission and processing.

4. The intelligent rail transit green wave driving method according to claim 3, characterized in that, In step S4, the permission level determination specifically involves: obtaining a preset priority score for the intelligent rail transit vehicle. Priority score for social vehicles and pedestrian priority score Calculate the weighted sum As shown in the following formula: In the formula, , , These are the dynamic weighting coefficients corresponding to intelligent rail vehicles, private vehicles, and pedestrians, respectively; based on the weighted sum... The calculation definition, in , , Under the preset dynamic changes, only when The green light acceleration operation is only triggered when the value exceeds the preset threshold. When triggered, calculate the green light acceleration duration. As shown in the following formula: In the formula, This represents the remaining duration of the current red light. The preset green light duration; Simultaneously verify the calculated green light acceleration time Whether the traffic light is within the preset acceleration threshold range is determined; a green light acceleration command is only generated and issued upon successful verification. When the green light acceleration command is issued, the traffic lights at the intersection are switched from the current red light state to an earlier state. It turns green after a certain time and remains green for at least the necessary time for the smart rail to completely pass through the intersection. Plus Duration.

5. The intelligent rail transit green wave driving method according to claim 1, characterized in that, Between step S2 and step S3, the method further includes: if the station monitoring system detects an interruption in the connected communication network link before sending data to the traffic signal management system, it temporarily stores the data in the data cache module and resends the data after the communication network link is restored; The network architecture for data transmission in this method includes: a 5G wireless network for communication between the intelligent rail vehicle unit and the station monitoring system; an optical fiber network for sequentially connecting and communicating with the station monitoring system, the traffic signal management system and the main traffic light control center; and a 4G LTE backup communication link that is automatically switched on when the optical fiber network is interrupted.

6. A smart rail transit green wave system implementing the method of any one of claims 1-5, characterized in that, The system comprises the following components: The intelligent rail vehicle unit is deployed on the intelligent rail vehicle to collect the real-time location and speed data of the intelligent rail vehicle and upload it to the station monitoring system through a wireless communication network. The station monitoring system is deployed along the intelligent rail line to verify the received real-time location and speed data, and based on the real-time location and preset intersection location information, to determine whether the intelligent rail vehicle has entered the intersection warning range. When it is determined that the vehicle has entered the intersection, the system calculates the estimated arrival time of the intelligent rail vehicle at the intersection and sends the data containing the estimated arrival time to the traffic signal management system. The traffic signal management system is deployed at traffic intersections to forward data containing the estimated arrival time at the intersection to the central traffic light control center, and to upload the current traffic light status and intersection traffic flow data. The central traffic light control center is used to determine whether to trigger a green light acceleration operation based on the estimated arrival time at the intersection, the status of the traffic lights, and the traffic flow data at the intersection, through a hierarchical judgment method. When triggered, it calculates the green light acceleration duration, generates a green light acceleration command, and sends it to the corresponding traffic light.

7. The intelligent rail transit green wave system according to claim 6, characterized in that, The intelligent rail vehicle unit includes: a multi-mode high-precision positioning module, an inertial navigation compensation module, and a 5G-V2X communication module; The multi-mode high-precision positioning module is used to collect the real-time position and velocity data using a preset sampling frequency; the inertial navigation compensation module is used to compensate the output of the multi-mode high-precision positioning module when the signal of the wireless communication network does not meet the preset requirements, so as to maintain the continuity of the real-time position and velocity data.

8. The intelligent rail transit green wave system according to claim 6, characterized in that, The station monitoring system includes: an intersection location database and a data caching module; The intersection location database is used to store the coordinate information of each traffic intersection along the intelligent rail line and the corresponding intersection warning range threshold; the data caching module is used to temporarily store communication data within a preset time when the communication network link between the station monitoring system and the traffic signal management system is interrupted, and to resend the data after the communication network link is restored.

9. The intelligent rail transit green wave system according to claim 6, characterized in that, The central traffic light control center includes: an edge computing module and a core algorithm module; The edge computing module is used to perform data verification, permission classification determination, and instruction generation operations; the core algorithm module has built-in quantitative formulas for calculating the estimated arrival time at the intersection, the green light acceleration time, and performing the permission classification determination.

10. The intelligent rail transit green wave system according to claim 6, characterized in that: The system also includes a dual transmission network architecture, consisting of a 5G wireless network, an optical fiber network, and a 4G LTE backup communication link. The 5G wireless network is used for data transmission between the intelligent rail vehicle unit and the station monitoring system. The optical fiber network is used for data transmission between the station monitoring system, the traffic signal management system, and the main traffic light control center. The 4G LTE backup communication link is used to automatically start when the optical fiber network is detected to be interrupted for more than a preset time threshold.

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