Expressway lane management and control method and system based on real-time traffic flow

Abstract

The invention discloses an expressway lane control method based on real-time traffic flow. The expressway lane control method comprises the following steps of performing fusion sensing through a distributed optical fiber vibration sensor array and a millimeter wave radar; constructing a space-time diagram convolutional network model, and dynamically mapping the road topological structure into a lane connection diagram with variable weight; a layered reinforcement learning architecture is adopted, and an emergency lane dynamic management mode is triggered; and deploying a digital twin sand table to perform multi-resolution simulation verification, and when the actual traffic flow deviates from a predicted value by more than 15%, starting a causal inference engine to backtrack a decision link and update an agent strategy library. According to the method, global dynamic lane optimization is realized through a multi-source perception fusion and hierarchical reinforcement learning architecture, simulation data shows that the morning peak traffic volume is improved by 29.3%, and the accident scene traffic efficiency is improved by 28.6%; a digital twinning and causal inference engine is adopted, the strategy updating response time is shortened to 30 seconds, and the congestion propagation distance is reduced by 35%.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic management, and in particular to a highway lane control method and system based on real-time traffic flow. Background Art

[0002] Traditional highway lane control mostly uses fixed speed limit signs or manual intervention, which has the following defects:

[0003] Delayed response: Static strategies based on historical data cannot respond to sudden traffic changes (such as accidents and holiday peaks) in real time.

[0004] Insufficient global coordination: Isolated regulation of a single lane can easily lead to chain congestion downstream (see comparative document CN114170793A).

[0005] Inefficient emergency resources: Emergency lanes are often illegally occupied, and traditional microwave detection has a high false alarm rate (reference document CN115512539A).

[0006] Existing technologies such as CN113870562B propose emergency lane control based on queue length, but do not solve the problems of multi-lane coordination and long-term speed stability.

[0007] The existing technology relies on a single sensor or static strategy, while the present invention achieves global dynamic optimization through multi-source perception fusion (vibration + radar) and hierarchical reinforcement learning architecture (long-term prediction + real-time control);

[0008] The present invention introduces a laser clearance scanner (infrared thermal imaging) and a causal reasoning engine into the emergency lane management to solve the problem of high false alarm rate of traditional microwave detection. Summary of the invention

[0009] The object of the present invention is to provide a highway lane control method and system based on real-time traffic flow to solve the problems raised in the above background technology.

[0010] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a highway lane control method based on real-time traffic flow, comprising the following steps: (a) through the fusion perception of distributed optical fiber vibration sensor array and millimeter wave radar, the vehicle trajectory, vehicle type distribution and vibration abnormal events of the entire road section are obtained in real time, and a multimodal traffic situation matrix is ​​generated; (b) a spatiotemporal graph convolutional network model is constructed to dynamically map the road topology structure into a lane connection graph with variable weights, and the edge weights between nodes are updated based on the real-time traffic gradient; (c) a hierarchical reinforcement learning architecture is adopted, the upper meta-agent predicts the optimal lane configuration strategy set within the next 1-6 hours based on the historical traffic pattern, and the lower lane agent adjusts the lane function mode and speed limit value combination at a time granularity of 30 seconds based on the Q-learning algorithm; (d) the dynamic management mode of the emergency lane is triggered. When the average vehicle speed on the main road is less than 25 km / h and lasts for more than 5 minutes, and there is no accident within 500 meters downstream, the laser clearance monitoring device is activated and the emergency lane is opened; (e) Deploy a digital twin sandbox for multi-resolution simulation verification. When the actual traffic flow deviates from the predicted value by more than 15%, the causal reasoning engine is started to backtrack the decision link and update the intelligent agent strategy library. The causal reasoning engine uses a Bayesian network model to backtrack abnormal nodes based on historical decision paths and real-time data. The nodes include historical decision paths, real-time traffic deviations, and external events such as weather. The edge weights are dynamically updated by the conditional probability table.

[0011] The method for generating the multimodal traffic situation matrix in step (a) includes: extracting the energy characteristics of the 0.5-5 Hz frequency band in the vibration signal by wavelet transform, and calculating the confidence of abnormal events in each lane segment according to the following formula:

[0012] Confidence=

[0013] in: Indicates the energy integral value of the high frequency band (3-5 Hz), Indicates the energy integral value of the low frequency band (0.5-2 Hz), is the smoothing coefficient, the value is 0.01;

[0014] When the confidence level exceeds 0.7, it is marked as a potential abnormal event and triggers the PTZ camera to take directional photos.

[0015] The reward function of the hierarchical reinforcement learning in step (c) is designed as:

[0016]

[0017] in: is the weight coefficient, is the travel time error weight coefficient, is the congestion propagation attenuation coefficient, is the speed limit deviation penalty coefficient; , , , , represents the downstream congestion entropy value, is the actual travel time in seconds, calculated based on real-time traffic flow. To predict the travel time, in seconds, generated by the historical data model of the upper meta-agent, To set a speed limit, is the actual average speed, is the congestion entropy attenuation coefficient, which is used to adjust the weight of the impact of the congestion entropy value on the reward. The value range is [0.1, 1.0]. After 500 Monte Carlo simulations, the weight coefficients of 0.3, 0.5, and 0.2 have been verified by simulation to reduce the travel time error by 18% and the congestion propagation by 35%.

[0018] When emergency lane occupation is detected, a penalty term is introduced:

[0019]

[0020] in: is the penalty factor, ranging from 0.1 to 0.5. is the occupation duration, is the maximum allowed occupancy threshold, set to 30 minutes.

[0021] The method for calculating the downstream congestion entropy value includes:

[0022] Divide the road units into 500-meter intervals, and first calculate the speed variation coefficient of each unit:

[0023]

[0024] in: Refers to the set of instantaneous speeds of all vehicles in the ith road unit, is the average instantaneous speed of vehicles in the i-th road unit, with a sampling window of 30 seconds. is the standard deviation of speed within the ii-th road unit, calculated based on the same time window;

[0025] Congestion propagation model based on information entropy theory:

[0026]

[0027] in: Indicates the total length of the affected area, which is the sum of the length of the current section and the length of the associated section within 2 kilometers downstream. The coordinated control of the entire road network is triggered when the CV value is low, such as 0.05, indicating that the speed fluctuation is small, such as all vehicles maintaining 50±2 km / h, indicating that the traffic flow is stable and close to the free flow state. A high CV value such as 0.3 indicates a large speed difference, such as the vehicle speed changes dramatically between 20-80 km / h, indicating congestion, accidents or frequent lane changes.

[0028] A highway lane control system based on real-time traffic flow, comprising:

[0029] Perception module:

[0030] Fiber optic vibration sensors, millimeter wave radars, and edge computing units;

[0031] The edge computing unit is connected to the millimeter wave radar and the optical fiber vibration sensor data through the 5G-V2X communication module;

[0032] The edge computing unit uses NVIDIA Jetson AGX Orin (computing power 275 TOPS), which supports real-time processing of multi-sensor data; Execution module: electromagnetic road spike array; Decision module: federated learning server cluster, using differential privacy aggregation strategy; Verification module: laser clearance scanner and variable speed limit sign, the laser clearance scanner (wavelength 905 nm) is integrated with an infrared thermal imaging module, which can distinguish between stationary obstacles and pedestrians, with a false detection rate of <0.1%.

[0033] The electromagnetic spike array comprises:

[0034] Magnetic adsorption unit, the magnetic field strength can be adjusted from 0.1 to 1.5 Tesla. When the magnetic field strength is 1.0 Tesla, it can resist the 8-level wind force to maintain the stable position of the road stud; Multi-spectrum LED unit, including 590nm amber and 630nm red dual bands, the color gamut covers 98% of sRGB; Anti-collision warning module, when the vehicle crosses the line speed> 20 km / h, the sound and light alarm is triggered. The sound and light alarm module supports decibel adjustment (80-105 dB), and the default setting is 90 dB.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention realizes global dynamic lane optimization through multi-source perception fusion and hierarchical reinforcement learning architecture. Simulation data shows that the traffic volume during peak hours increases by 29.3%, and the traffic efficiency in accident scenarios increases by 28.6%. By adopting digital twins and causal reasoning engines, the strategy update response time is shortened to 30 seconds, and the congestion propagation distance is reduced by 35%. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the method steps of the present invention;

[0038] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0039] Figure 3 It is a schematic diagram of the system flow of the present invention;

[0040] Figure 4 This is a data fusion flow chart of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1-4 The present invention provides a technical solution: a highway lane control method and system based on real-time traffic flow.

[0043] Embodiment 1:

[0044] Multi-source perception data fusion

[0045] Data collection:

[0046] Distributed fiber optic vibration sensors (model OSEN-FT-01) are deployed every 20 meters along the central median strip, with a sampling rate of 1kHz and a sensitive frequency band of 0.5-5 Hz. They can identify vehicle type (such as trucks, cars), vehicle speed, and abnormal events (such as sudden braking, collision) by detecting ground vibration signals;

[0047] A 79 GHz millimeter-wave radar is installed on the roadside with a horizontal field of view of ±60°, a detection distance of 200 m, and outputs target coordinates and speed information.

[0048] Vibration signal processing flow Discrete wavelet decomposition of optical fiber vibration signal: select Daubechies 4 wavelet basis function; decomposition level: 3 levels;

[0049] Obtain the approximate coefficient cA and the detail coefficient cD;

[0050] Calculate high frequency energy ;

[0051] Calculate low frequency energy ;

[0052] Calculate the anomaly confidence level according to the formula:

[0053] When the confidence level is greater than 0.7, the PTZ camera is triggered to capture and save images to verify abnormal events (such as accidents and breakdowns).

[0054] Radar data processing flow:

[0055] Perform spatiotemporal alignment on radar point cloud data to generate vehicle trajectory point set ;

[0056] The density clustering algorithm (DBSCAN) is used for trajectory classification. The Euclidean distance formula of the density clustering algorithm is:

[0057] Neighborhood radius ;

[0058] Minimum number of neighborhood points minPts=5;

[0059] Output the number of vehicles, average speed and acceleration distribution in each lane.

[0060] Data fusion logic establishes vibration-radar association rules:

[0061] Data association rules:

[0062] The vibration signal is aligned with the radar trajectory in time and space to establish a vibration-radar correlation matrix (e.g., the abnormal area of ​​the vibration signal is matched with the vehicle emergency braking position detected by the radar).

[0063] Generate a multimodal traffic situation matrix with the following dimensions:

[0064] Lane-level traffic density;

[0065] Distribution ratio of vehicle types (proportion of trucks, proportion of small cars);

[0066] Abnormal event location and confidence level;

[0067] Real-time velocity gradient (upstream and downstream velocity difference).

[0068] Dynamic lane topology modeling and traffic flow prediction

[0069] Spatio-Temporal Graph Convolutional Network (ST-GCN):

[0070] The road topology is mapped into a graph structure, where nodes represent lane units and edges represent lane connection relationships.

[0071] Dynamic weight update: adjust edge weights according to real-time traffic gradients (such as a sudden increase in upstream traffic). The formula is:

[0072] in, is the flow gradient, Connect the edges for the lanes;

[0073] Output: A lane connectivity map with variable weights, identifying areas of high congestion risk.

[0074] Hierarchical Reinforcement Learning Decision Making:

[0075] Upper-level meta-agent:

[0076] enter:

[0077] Historical traffic patterns (e.g. weekday morning rush hour traffic), weather data, event calendars.

[0078] Model:

[0079] Based on the LSTM network, the optimal lane configuration strategy set for the next 1-6 hours is predicted (such as the opening time of tidal lanes and the pre-opening period of emergency lanes).

[0080] Lower lane agent:

[0081] enter:

[0082] Real-time traffic situation matrix and upper-level policy constraints.

[0083] algorithm:

[0084] Real-time optimization based on Q-learning adjusts the combination of lane functions (such as variable lane direction switching) and speed limit values ​​every 30 seconds.

[0085] Reward function design:

[0086]

[0087] Weight combination α, β, γ Improved traffic efficiency Congestion transmission reduction (0.3,0.5,0.2) 29.3% 35% (0.2,0.6,0.2) 24.1% 28% (0.4,0.4,0.2) 18.7% 22%

[0088] This table is a comparison table of experimental data.

[0089] Dynamic management of emergency lanes

[0090] Trigger conditions:

[0091] Main road congestion: the average vehicle speed on the main road is less than 25 km / h for 5 minutes (based on the Greenshields model to avoid misjudgment of instantaneous fluctuations);

[0092] Downstream safety: No accidents within 500 meters downstream (verified in real time by cameras and radars).

[0093] Execution: Activate the laser clearance scanner (wavelength 905 nm) and combine it with infrared thermal imaging to detect obstacles in the emergency lane. The false detection rate is <0.1%. Based on 1000 hours of real road test data, a total of 12,345 obstacles were detected and 12 false alarms were reported.

[0094] The emergency lane is opened and switched to red flashing mode (3 Hz) through electromagnetic road spikes, and the variable speed limit sign is updated synchronously;

[0095] If a vehicle illegally occupies the road (crossing the road at a speed > 20 km / h), a 105 dB sound and light alarm (frequency 2-4 kHz) will be triggered.

[0096] Embodiment 2:

[0097] Congestion entropy calculation and control

[0098] Divide the 10 km section into 20 500-meter units;

[0099] Real-time calculation of the speed variation coefficient of each unit (sampling window 30 seconds);

[0100] When the total congestion entropy When , the following linkage control is triggered:

[0101] The speed limit of the upstream unit decreases by 10% (e.g. 100 → 90 km / h);

[0102] parameter Setting Value Car following model Intelligent Driver Model (IDM) Maximum acceleration 1.5 m / s² Safe headway 1.8 s Monte Carlo simulation times 500 times

[0103] This table is simulation parameter data.

[0104] Example 3: Implementation of electromagnetic spikes

[0105] The distance between spikes is 5 meters. The magnetic unit uses NdFeB permanent magnets (grade N52). The magnetic field strength is 1.0 T after power is turned on.

[0106] Multi-spectrum LED unit display strategy:

[0107] Green: The lane is open to traffic;

[0108] Flashing red (3 Hz): Lane closed;

[0109] Solid amber: Tidal lane is enabled;

[0110] Scenario Traditional method (traffic volume / hour) The present invention (traffic volume / hour) Improvement rate Morning rush hour 2,150 2,780 +29.3% Accident handling 1,890 2,430 +28.6%

[0111] This table contains simulation test (SUMO platform) data.

[0112] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

Claims

1. A highway lane control method based on real-time traffic flow, characterized in that: The following steps are involved: (a) Through the fusion perception of distributed optical fiber vibration sensor array and millimeter wave radar, the vehicle trajectories, vehicle type distribution and vibration abnormal events of the entire road section are obtained in real time to generate a multi-modal traffic situation matrix; (b) Construct a spatiotemporal graph convolutional network model to dynamically map the road topology into a lane connection graph with variable weights, and update the edge weights between nodes based on real-time traffic gradients; (c) Using a hierarchical reinforcement learning architecture, the upper meta-agent predicts the optimal lane configuration strategy set within the next 1-6 hours based on historical traffic patterns, and the lower lane agent adjusts the lane function mode and speed limit value combination at a 30-second time granularity based on the Q-learning algorithm; (d) Trigger the dynamic management mode of the emergency lane. When the average vehicle speed on the main road is less than 25 km / h for more than 5 minutes and there is no accident within 500 meters downstream, the laser clearance monitoring device is activated and the emergency lane is opened; (e) Deploy a digital twin sandbox for multi-resolution simulation verification. When the actual traffic flow deviates from the predicted value by more than 15%, the causal reasoning engine is started to backtrack the decision link and update the intelligent agent strategy library. The causal reasoning engine uses a Bayesian network model to backtrack abnormal nodes based on historical decision paths and real-time data. The nodes include historical decision paths, real-time traffic deviations, and external events such as weather. The edge weights are dynamically updated by the conditional probability table.

2. A highway lane control method based on real-time traffic flow according to claim 1, characterized in that: The method for generating the multimodal traffic situation matrix in step (a) includes: The energy characteristics of the 0.5-5 Hz frequency band in the vibration signal are extracted by wavelet transform, and the confidence of abnormal events in each lane section is calculated according to the following formula: in: Represents the energy integral value of the high frequency band, Indicates the energy integral value of the low frequency band, is the smoothing coefficient, the value is 0.01; When the confidence level exceeds 0.7, it is marked as a potential abnormal event and triggers the PTZ camera to take directional photos.

3. A highway lane control method based on real-time traffic flow according to claim 1, characterized in that: The reward function of the hierarchical reinforcement learning in step (c) is designed as: in: is the weight coefficient, is the travel time error weight coefficient, is the congestion propagation attenuation coefficient, is the speed limit deviation penalty coefficient; , , , , represents the downstream congestion entropy value, is the actual travel time in seconds, calculated based on real-time traffic flow. To predict the travel time, in seconds, generated by the historical data model of the upper meta-agent, To set a speed limit, is the actual average vehicle speed, is the congestion entropy attenuation coefficient, which is used to adjust the weight of the impact of the congestion entropy value on the reward. The value range is [0.1, 1.0]. After 500 Monte Carlo simulations, the weight coefficients of 0.3, 0.5, and 0.2 have been verified by simulation to reduce the travel time error by 18% and the congestion propagation by 35%. When emergency lane occupation is detected, a penalty term is introduced: in: is the penalty factor, ranging from 0.1 to 0.

5. is the occupation duration, is the maximum allowed occupancy threshold, set to 30 minutes.

4. A highway lane control method based on real-time traffic flow according to claim 3, characterized in that: The method for calculating the downstream congestion entropy value includes: Divide the road units into 500-meter intervals, and first calculate the speed variation coefficient of each unit: in: Refers to the set of instantaneous speeds of all vehicles in the ith road unit, is the average instantaneous speed of vehicles in the i-th road unit, with a sampling window of 30 seconds. is the standard deviation of speed within the ii-th road unit, calculated based on the same time window; Congestion propagation model based on information entropy theory: in: Indicates the total length of the affected area. The coordinated control of the entire road network is triggered when the CV value is low, such as 0.05, indicating that the speed fluctuation is small, such as all vehicles maintaining 50±2 km / h, indicating that the traffic flow is stable and close to the free flow state. A high CV value such as 0.3 indicates a large speed difference, such as the vehicle speed changes dramatically between 20-80 km / h, indicating congestion, accidents or frequent lane changes.

5. A highway lane control system based on real-time traffic flow, characterized in that: include: Perception module: optical fiber vibration sensor, millimeter wave radar and edge computing unit, the edge computing unit is connected to the millimeter wave radar and optical fiber vibration sensor data through the 5G-V2X communication module; Execution module: electromagnetic spike array; Decision module: Federated learning server cluster, using differential privacy aggregation strategy; Verification module: laser clearance scanner and variable speed limit sign. The laser clearance scanner integrates an infrared thermal imaging module, which can distinguish between stationary obstacles and pedestrians, with a false detection rate of <0.1%; The edge computing unit uploads the preprocessed multimodal traffic situation matrix to the federated learning server cluster through the 5G-V2X communication module, and the federated learning server cluster broadcasts lane control instructions to the electromagnetic road spike array through 5G-V2X. The federated learning server cluster sends control signals to the laser clearance scanner and the variable speed limit sign through the edge computing unit, and the laser clearance scanner and the variable speed limit sign transmit the real-time status back to the federated learning server through 5G-V2X.

6. A highway lane control system based on real-time traffic flow according to claim 5, characterized in that: The electromagnetic spike array comprises: Magnetic adsorption unit, the magnetic field strength can be adjusted from 0.1 to 1.5 Tesla. When the magnetic field strength is 1.0 Tesla, it can resist the 8-level wind force to maintain the stable position of the road spikes. Multi-spectrum LED unit, including 590nm amber and 630nm red dual bands, color gamut covers 98% of sRGB; The anti-collision warning module triggers an audible and visual alarm when the vehicle crosses the line at a speed greater than 20 km / h. The audible and visual alarm module supports decibel adjustment, and the default setting is 90 dB.

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