A method and system for controlling mobile temporary traffic lights for traffic safety.

Abstract

This invention relates to the field of traffic signal control and traffic safety technology, specifically to a mobile temporary traffic light control method and system for traffic safety. The method includes: real-time acquisition of the queue length and average density of the waiting lanes upstream of the traffic light, and acquisition of the average vehicle speed and road saturation of the downstream road segment; calculation of the upstream traffic demand index based on the acquired queue length and average density; calculation of the downstream congestion risk index based on the average vehicle speed and road saturation; calculation of the control mode switching index by combining the upstream traffic demand index and the downstream congestion risk index; comparison of the control mode switching index with a preset switching threshold to determine the current control mode; generation of the final green light duration based on the determined current control mode; and output of the control command corresponding to the final green light duration. This invention ensures a smooth transition and continuous effectiveness of traffic control strategies, improving the driving experience and the stability of road network operation.

Description

Technical Field

[0001] This invention relates to the field of traffic signal control and traffic safety technology, specifically to a method and system for controlling mobile temporary traffic lights for traffic safety. Background Technology

[0002] As urban traffic management becomes increasingly complex, mobile temporary traffic lights play a crucial role in scenarios such as road construction, traffic accident handling, and large-scale events. These temporary traffic lights need to be deployed quickly and effectively manage traffic.

[0003] Currently, most traditional mobile temporary traffic lights use fixed timing schemes or simple vehicle-sensor control. Fixed timing schemes cannot be adjusted according to real-time traffic flow changes, resulting in low traffic dispersal efficiency during peak hours and potentially causing unnecessary empty runs and delays during off-peak hours. While simple sensor control can detect the presence of waiting vehicles, its decision-making is limited to local traffic information upstream of the traffic light, lacking consideration for the downstream road network's carrying capacity. This control method may release a large amount of traffic flow to already saturated or impending congestion downstream sections while dispersing upstream vehicles, not only failing to effectively solve the congestion problem but also potentially triggering regional traffic congestion or even worsening traffic conditions. Therefore, how to achieve a system that can dynamically balance upstream traffic demand and downstream congestion risk, and intelligently adjust green light duration to ensure traffic efficiency while avoiding exacerbating downstream traffic congestion, has become a pressing technical problem to be solved in this field. Summary of the Invention

[0004] The purpose of this invention is to provide a mobile temporary traffic light control method and system for traffic safety, which can dynamically balance upstream traffic demand and downstream congestion risk, and intelligently adjust the green light duration, thereby ensuring traffic efficiency while avoiding exacerbating downstream traffic congestion. Specifically, the technical solution of this invention is as follows:

[0005] A method for controlling a mobile temporary traffic light for traffic safety includes:

[0006] Real-time data collection of queue length and average density of waiting lanes upstream of traffic lights, and acquisition of average vehicle speed and road saturation downstream of traffic lights;

[0007] The upstream passage demand index is calculated based on the collected queue length and average density.

[0008] Calculate the downstream congestion risk index based on average vehicle speed and road saturation;

[0009] The control mode switching index is calculated by combining the upstream traffic demand index and the downstream congestion risk index.

[0010] The control mode switching index is compared with a preset switching threshold to determine the current control mode;

[0011] Based on the determined current control mode, generate the final green light duration;

[0012] Output the control command corresponding to the final green light duration.

[0013] Preferably, the current control mode includes:

[0014] If the control mode switching index is greater than the switching threshold, the current control mode is determined to be the local optimal mode.

[0015] If the control mode switching index is not greater than the switching threshold, the current control mode is determined to be the global safety mode.

[0016] Preferably, the final green light duration includes:

[0017] Based on queue length and average density, calculate the estimated number of vehicles in the queue;

[0018] Based on the estimated number of vehicles, the theoretically optimal green light duration is calculated using a vehicle saturation flow departure model.

[0019] In response to the current control mode being the local optimal mode, the theoretically optimal green light duration is determined as the final green light duration;

[0020] In response to the current control mode being global safety mode, the corrected green light duration is calculated and determined as the final green light duration.

[0021] Preferably, the calculation of the corrected green light duration includes:

[0022] The difference between the theoretically optimal green light duration and the preset minimum safe green light duration is determined as the compressible elastic duration.

[0023] Calculate the attenuation factor based on the downstream congestion risk index;

[0024] Multiply the compressible elastic duration by the attenuation factor to obtain the corrected elastic duration;

[0025] The corrected green light duration is generated by adding the adjusted flexible duration to the minimum safe green light duration.

[0026] Preferably, the method further includes:

[0027] In response to the current control mode being global safety mode, the control mode switching index is compared with a preset recovery threshold, wherein the recovery threshold is greater than the switching threshold;

[0028] When the control mode switching index exceeds the recovery threshold, the current control mode is switched from the global safe mode to the local optimal mode.

[0029] If the control mode switching index is not greater than the recovery threshold, the current control mode is maintained as the global safety mode.

[0030] A mobile temporary traffic light control system for traffic safety includes:

[0031] The traffic parameter acquisition module is used to collect the queue length and average density of the waiting lane upstream of the traffic light in real time, and to obtain the average vehicle speed and road saturation of the road section downstream of the traffic light.

[0032] The state quantification module is used to calculate the upstream traffic demand index based on the collected queue length and average density, and to calculate the downstream congestion risk index based on the average vehicle speed and road saturation.

[0033] The control mode switching assessment module is used to combine the upstream traffic demand index and the downstream congestion risk index to calculate the control mode switching index, and compare the control mode switching index with the preset switching threshold to determine the current control mode.

[0034] The signal timing module is used to generate the final green light duration based on the determined current control mode and output the corresponding control command.

[0035] Preferably, the control switching evaluation module is used for:

[0036] If the control mode switching index is greater than the switching threshold, the current control mode is determined to be the local optimal mode.

[0037] If the control mode switching index is not greater than the switching threshold, the current control mode is determined to be the global safety mode.

[0038] Preferably, the signal timing module includes:

[0039] The time benchmark calculation unit is used to calculate the theoretically optimal green light duration based on the queue length and average density, using a vehicle saturation flow departure model.

[0040] The timing strategy application unit is used to determine the theoretically optimal green light duration as the final green light duration in response to the current control mode being the local optimal mode, and to calculate the corrected green light duration as the final green light duration in response to the current control mode being the global safety mode.

[0041] Preferred options also include:

[0042] The closed-loop adaptive correction module is used to compare the control mode switching index with the preset recovery threshold in response to the current control mode being the global safety mode, where the recovery threshold is greater than the switching threshold.

[0043] The closed-loop adaptive correction module is also used to switch the current control mode from the global safe mode to the local optimal mode in response to the control mode switching index being greater than the recovery threshold, and to maintain the current control mode as the global safe mode in response to the control mode switching index not being greater than the recovery threshold.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. This application can comprehensively perceive the traffic status of the road network by collecting and quantifying upstream traffic demand and downstream congestion risk in real time. This global perspective overcomes the limitations of traditional control that only relies on local upstream information, making traffic light timing decisions more scientific and predictive, thereby effectively improving the overall efficiency of traffic management.

[0046] 2. This application has created a unique dynamic switching mechanism between two control modes: local optimal and global safety. It can intelligently balance the two objectives of dispersing upstream queues and avoiding downstream congestion based on real-time traffic conditions. It can maximize traffic efficiency when downstream is smooth and actively suppress traffic flow when downstream is congested, which significantly improves the adaptability of traffic lights to complex traffic environments.

[0047] 3. When the risk of congestion is predicted downstream, this application can actively and dynamically adjust the green light duration. It does not simply shorten the time, but calculates the optimal amount of suppression based on the degree of risk. Under the premise of ensuring basic traffic safety, it accurately controls the amount of traffic released upstream, effectively preventing the chain reaction of downstream traffic conditions deteriorating due to the concentrated release of vehicles upstream.

[0048] 4. By introducing hysteresis comparison logic where the recovery threshold is greater than the switching threshold, this application effectively avoids frequent and unstable mode switching of the control system due to small fluctuations in traffic flow near the congestion threshold. This design enhances the robustness of the control system, ensures the smooth transition and continuous effectiveness of traffic control strategies, and improves the driving experience and the stability of road network operation. Attached Figure Description

[0049] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0050] Figure 1 This is a flowchart of the method of the present invention;

[0051] Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0053] Example 1:

[0054] Please see Figure 1 A method for controlling mobile temporary traffic lights for traffic safety, comprising:

[0055] Real-time data collection of queue length and average density of waiting lanes upstream of traffic lights, and acquisition of average vehicle speed and road saturation downstream of traffic lights;

[0056] The upstream passage demand index is calculated based on the collected queue length and average density.

[0057] Calculate the downstream congestion risk index based on average vehicle speed and road saturation;

[0058] The control mode switching index is calculated by combining the upstream traffic demand index and the downstream congestion risk index.

[0059] The control mode switching index is compared with a preset switching threshold to determine the current control mode;

[0060] Based on the determined current control mode, generate the final green light duration;

[0061] Output the control command corresponding to the final green light duration.

[0062] This invention provides a method for controlling mobile temporary traffic lights for traffic safety.

[0063] This method collects real-time data on the queue length and average density of waiting lanes upstream of the traffic light, and obtains the average vehicle speed and road saturation of the downstream section. Here, upstream refers to the direction of traffic flow approaching the traffic light, i.e., the section where vehicles are queuing to pass; downstream refers to the direction of traffic flow leaving the traffic light, i.e., the section where vehicles continue driving after passing the traffic light. The purpose of this step is to comprehensively and in real-time perceive the traffic conditions most relevant to traffic light control decisions. In this embodiment, the real-time queue length collected by local sensors... Compared with the real-time average queue density collected and calculated by local sensors High-frequency data collection is achieved through local sensors such as lidar or millimeter-wave radar deployed on mobile temporary traffic lights; specifically, The calculation method is as follows: A specific detection area in the waiting lane is identified using lidar. This specific detection area refers to a fixed region upstream of the traffic light in the waiting lane designated for collecting vehicle data. For example, the number of vehicle targets within 150 meters behind the stop line. Then divide that number by the length of the detection area. ,Right now ;

[0064] Based on the collected real-time queue length and average queue density, the upstream traffic demand index is calculated; the purpose of this step is to transform the raw upstream traffic data into a standardized decision variable that directly reflects the urgency of evacuation; in this embodiment, The calculation method is as follows:

[0065]

[0066] in: This is the upstream passage demand index calculated in this step; The maximum queue length that the lane design can accommodate is predetermined based on road design specifications or actual measurements; This refers to the maximum road design density preset according to road design specifications. These are weighting coefficients, summing to 1. Their values ​​are determined using the following multiple linear regression method based on historical data: Data is collected including data under different traffic conditions. Data and corresponding expert assessment scores for the urgency of passage. The training dataset is in the range of 0-1; As the dependent variable, and Establish a regression model with the variable as the independent variable. The optimal weighting coefficients are obtained by solving the least squares method. ;

[0067] Based on average vehicle speed and road saturation, a downstream congestion risk index is calculated; the purpose of this step is to quantify the degree of deterioration in the downstream road network's capacity to accommodate newly arriving traffic flows; in this embodiment... The calculation method is as follows:

[0068] =

[0069] in: This is the downstream congestion risk index calculated in this step; The real-time average vehicle speed of downstream road sections is obtained through data interfaces or vehicle-to-everything (V2X) networks. The free-flow speed is preset according to the road grade; This refers to the real-time saturation of downstream road sections obtained through data interfaces or vehicle-to-everything (V2X) networks. Data can be obtained from downstream vehicles or roadside units through data interfaces with regional traffic management platforms or through V2x communication modules. These are weighting coefficients, summing to 1. They are obtained through offline traffic simulation calibration. The dataset used in this calibration process is independent of the variables used during model runtime. The specific steps are as follows: Construct a microscopic traffic simulation model including traffic lights and their downstream road segments, for example, using SUMO or Vissim; Design multiple simulation scenarios covering different downstream traffic flows and events, such as bottlenecks and accidents; Define a congestion evaluation function, for example... ,in For the average downstream travel delay, This represents the number of times congestion overflows upstream; a and b are weighting coefficients, whose values ​​are determined based on the degree of attention traffic management pays to traffic delays and congestion overflow. The congestion overflow equivalent delay coefficient, with dimensions of time / number of occurrences, is used to convert the frequency of congestion overflow events into equivalent travel delay times. This coefficient can be determined based on the average impact duration of a single overflow event on the road network, as calibrated in historical data. A grid search method is employed. Under the constraints, traverse different Combine and run simulations of each scenario; calculate the evaluation function for all scenarios under each weight group. The average value, select the one that makes the average The weighted combination that has the highest correlation between the values ​​and the actual observed congestion phenomena, that is, the combination of different values ​​used in the simulation. Downstream congestion risk index obtained by combined calculation The sequence, and the corresponding evaluation function for this simulation scenario. Perform statistical correlation analysis on the series of values ​​and select the series with the strongest correlation. combination.

[0070] By combining the upstream traffic demand index and the downstream congestion risk index, a control mode switching index is calculated. The purpose of this step is to establish a unified decision criterion that can dynamically weigh the benefits of allowing traffic flow against the risks of congestion. In this embodiment, The calculation method is as follows:

[0071]

[0072] in: This is the control mode switching index calculated in this step; The upstream traffic demand index is a standardized decision variable that transforms raw upstream traffic data into a direct reflection of the urgency of evacuation. The risk aversion coefficient is an adjustable parameter greater than 1, for example, a value of 2. Its value is calibrated through offline simulation based on the preference for avoiding traffic safety risks. The denominator uses an exponential form because when... When it grows, this item can grow rapidly and non-linearly, thus punishing downstream congestion more severely, reflecting a high risk aversion preference for the overall security of the system;

[0073] The control mode switching index calculated in the previous steps is compared with the preset switching threshold to determine the current control mode; This is a preset constant, such as 0.5, whose value is determined by: calibrating through extensive offline simulations or actual road tests to find a critical point that effectively balances traffic efficiency and congestion risk; the specific calibration method is: using the same offline traffic simulation model as described above; setting a series of values ​​to be calibrated. Candidate values, for example, from 0.1 to 1.0, with a step size of 0.05; for each candidate value, run a long-term simulation scenario that includes the dynamic evolution of traffic flow from smooth to congested and then back to smooth; define the system's comprehensive performance indicators, for example:

[0074]

[0075] in, This represents the total upstream traffic volume. The total duration for which the downstream road saturation exceeds 0.9; It is a congestion impact conversion factor with the dimension of number of vehicles / time. This factor can be calibrated according to parameters such as the design capacity or saturation flow rate of the downstream road section. It is used to convert the congestion duration into an equivalent number of affected vehicles, thereby ensuring the uniformity of the entire formula in terms of dimensions. For performance balancing weights; Assess risk balancing weights; select indicators that maximize the overall system performance. The candidate value that reaches the maximum value is used as the final switching threshold. If the calculated Value not greater than This indicates that the control mode needs to be switched;

[0076] Based on the determined current control mode, the final green light duration is generated; this step transforms the macro-level control mode decision into specific signal timing parameters.

[0077] The control command corresponding to the final green light duration is output. This command is encapsulated in a format that conforms to the traffic light controller communication protocol and sent to the hardware execution unit of the mobile temporary traffic light via wired or wireless means.

[0078] Example 2:

[0079] Current control modes include:

[0080] If the control mode switching index is greater than the switching threshold, the current control mode is determined to be the local optimal mode.

[0081] If the control mode switching index is not greater than the switching threshold, the current control mode is determined to be the global safety mode.

[0082] This embodiment provides a detailed explanation of the steps for determining the current control mode;

[0083] When the control mode switching exponent exceeds the switching threshold, the current control mode is determined to be the local optimal mode; ModeL is a mode whose primary control objective is to maximize the upstream queue evacuation efficiency; when When the system determines that the downstream congestion risk is low or the upstream evacuation demand is extremely urgent, the traffic benefits brought by releasing vehicles far outweigh the risk of exacerbating downstream congestion. Therefore, the system chooses to remain in ModeL.

[0084] If the control mode switching index is not greater than the switching threshold, the current control mode is determined to be the global safety mode; ModeG is a mode whose primary control objective is to prioritize ensuring the stability of the downstream road network and avoid congestion spread; when When the system determines that the downstream congestion risk has reached or exceeded the critical level, if it continues to release traffic at maximum efficiency, it is very likely that the downstream traffic situation will deteriorate rapidly. Therefore, the system switches to ModeG and takes measures to suppress upstream traffic.

[0085] Example 3:

[0086] The final green light duration is generated, including:

[0087] Based on queue length and average density, calculate the estimated number of vehicles in the queue;

[0088] Based on the estimated number of vehicles, the theoretically optimal green light duration is calculated using a vehicle saturation flow departure model.

[0089] In response to the current control mode being the local optimal mode, the theoretically optimal green light duration is determined as the final green light duration;

[0090] In response to the current control mode being global safety mode, the corrected green light duration is calculated and determined as the final green light duration.

[0091] This embodiment details the steps for generating the final green light duration;

[0092] Based on queue length and average density, the estimated number of vehicles in the queue is calculated. The calculation method is as follows:

[0093]

[0094] in and This represents the data collection results from the preceding steps;

[0095] Based on the estimated number of vehicles, the theoretically optimal green light duration is calculated using a vehicle saturation flow departure model. The purpose of this step is to calculate the basic green light time required to completely clear the current waiting queue under ideal conditions. The calculation method is as follows:

[0096]

[0097] in: This is the theoretically optimal green light duration calculated in this step; This is an empirical constant preset based on the characteristics of the stop line at the intersection, for example, 2 seconds; This is an empirical constant preset based on road grade, such as 2.5 seconds per vehicle; This is the result of the calculation in the previous step;

[0098] Applying timing strategy: In response to the current control mode being the local optimal mode, the theoretically optimal green light duration is determined as the final green light duration; under this mode, the final green light duration... Directly adopt the calculated ;

[0099] In response to the current control mode being global safety mode, the corrected green light duration is calculated and determined as the final green light duration. In this mode, the system activates the correction model to calculate a suppressed and shortened green light duration. As the final green light duration.

[0100] Example 4:

[0101] The calculation of the corrected green light duration includes:

[0102] The difference between the theoretically optimal green light duration and the preset minimum safe green light duration is determined as the compressible elastic duration.

[0103] Calculate the attenuation factor based on the downstream congestion risk index;

[0104] Multiply the compressible elastic duration by the attenuation factor to obtain the corrected elastic duration;

[0105] The corrected green light duration is generated by adding the adjusted flexible duration to the minimum safe green light duration.

[0106] This embodiment elaborates in detail on the technical features of calculating the corrected green light duration;

[0107] The difference between the theoretically optimal green light duration and the preset minimum safe green light duration is determined as the compressible elastic duration. The minimum green light time is determined by traffic regulations or safety standards, such as 5 seconds; a compressible, flexible duration is also possible. It clarifies the portion of the green light duration that can be adjusted without violating safety guidelines;

[0108] Based on the downstream congestion risk index, an attenuation factor is calculated; in this embodiment, the attenuation factor is designed as an exponential decay function. ;in The suppression coefficient, calibrated through offline simulation, is used to control the decay rate; for example, a value of 1.5 is taken. When the value is 0, the attenuation factor is 1; when When the factor increases, it rapidly approaches 0 in an exponential manner, thus producing a strong inhibitory effect.

[0109] Multiplying the compressible elastic duration by the attenuation factor yields the corrected elastic duration, i.e. ;

[0110] The adjusted green light duration is generated by adding the adjusted flexible duration to the minimum safe green light duration. To ensure that the final duration does not violate the safety baseline, the complete generation logic is modified as follows:

[0111] Determine the theoretically optimal green light duration Minimum safe green light duration Relationship:

[0112] like Therefore, the minimum safe green light duration will be directly determined as the corrected green light duration, i.e. ;like The corrected green light duration is then calculated using the following formula: The complete calculation formula is as follows:

[0113]

[0114] in and These are the calculation results from the preceding steps; combined with the aforementioned judgment steps, this logic ensures that, under any circumstances, the final generated green light duration is consistent. The green light duration will not be less than the preset minimum safe green light duration. Under the premise that the theoretical green light duration is greater than the minimum safe green light duration, when When the value is extremely large, the result calculated by the above formula will be infinitely close to... This effectively suppressed the release of goods from the upstream.

[0115] Example 5:

[0116] This method also includes:

[0117] In response to the current control mode being global safety mode, the control mode switching index is compared with a preset recovery threshold, wherein the recovery threshold is greater than the switching threshold;

[0118] When the control mode switching index exceeds the recovery threshold, the current control mode is switched from the global safe mode to the local optimal mode.

[0119] If the control mode switching index is not greater than the recovery threshold, the current control mode is maintained as the global safety mode.

[0120] This embodiment adds a closed-loop adaptive correction logic for the control mode to solve the problem of high-frequency and unstable mode switching that may occur near the congestion relief threshold; this logic introduces the principle of hysteresis comparison.

[0121] When the system is already in global security mode (ModeG), the criteria for mode switching will change; at this time, the system will compare the control mode switching index with the preset recovery threshold. The setting method is as follows:

[0122]

[0123] in: The recovery threshold is calculated in this step; The preset switching threshold; This is a preset constant greater than 1, such as 1.2; by introducing... The coefficient ensures ;

[0124] Based on this recovery threshold, the system executes the following switching logic: In response to the control mode switching index exceeding the recovery threshold, the current control mode is switched from the global safe mode to the local optimal mode; this means that only when traffic conditions significantly improve, such that… Value rebounded and surpassed a higher threshold. Only then is the system allowed to release the suppression and switch back to ModeL;

[0125] In response to the control mode switching index not exceeding the recovery threshold, the current control mode is maintained as the global safety mode; even if The value has rebounded from its lowest point and is slightly above [the previous value]. However, as long as it fails to reach The system will then remain in Mode G.

[0126] Example 6:

[0127] Please see Figure 2 A mobile temporary traffic light control system for traffic safety includes:

[0128] The traffic parameter acquisition module is used to collect the queue length and average density of the waiting lane upstream of the traffic light in real time, and to obtain the average vehicle speed and road saturation of the road section downstream of the traffic light.

[0129] The state quantification module is used to calculate the upstream traffic demand index based on the collected queue length and average density, and to calculate the downstream congestion risk index based on the average vehicle speed and road saturation.

[0130] The control mode switching assessment module is used to combine the upstream traffic demand index and the downstream congestion risk index to calculate the control mode switching index, and compare the control mode switching index with the preset switching threshold to determine the current control mode.

[0131] The signal timing module is used to generate the final green light duration based on the determined current control mode and output the corresponding control command.

[0132] This invention provides a mobile temporary traffic light control system for traffic safety, the system comprising:

[0133] The traffic parameter acquisition module aims to provide real-time and accurate raw data input for the entire control system. This module integrates sensors such as lidar and millimeter-wave radar to collect real-time data on the waiting lanes upstream of the traffic lights. and Simultaneously, this module also integrates a 4G / 5G or dedicated short-range communication (DSRC) module for acquiring information about the downstream road segment of the traffic light. and For example, when interacting with the regional traffic management platform through a 4G / 5G module, the platform's RESTful API can be called via the HTTPS protocol to obtain downstream road segment data, which can be in JSON format.

[0134] The state quantification module aims to process the collected multi-source, heterogeneous raw traffic data into standardized, decision-making quantification indices. This module receives data from the traffic parameter acquisition module and, based on the collected data... and Using the formula calculate At the same time, it is also based on and Using the formula calculate ;

[0135] The control switching evaluation module aims to dynamically determine macro-control strategies based on the quantified system state; this module receives data from the state quantization module. and Combining the two through a nonlinear formula calculate ; will Values ​​and presets Compare these to determine which control mode should be used at the current time;

[0136] The purpose of the signal timing module is to transform macro-level control mode decisions into specific traffic light control commands and execute them. This module receives the current control mode determined by the control switching evaluation module, generates the final green light duration based on the mode, and finally outputs control commands that conform to the traffic light hardware protocol.

[0137] Example 7:

[0138] The control switching evaluation module is used for:

[0139] If the control mode switching index is greater than the switching threshold, the current control mode is determined to be the local optimal mode.

[0140] If the control mode switching index is not greater than the switching threshold, the current control mode is determined to be the global safety mode.

[0141] This embodiment further refines the functions of the control switching evaluation module;

[0142] When the module calculates At this time, the decision logic inside the module will determine that the current control mode is ModeL and output this mode identifier to the signal timing module;

[0143] When the module calculates At that time, the decision logic inside the module will determine the current control mode as ModeG and output this mode identifier to the signal timing module.

[0144] Example 8:

[0145] The signal timing module includes:

[0146] The time benchmark calculation unit is used to calculate the theoretically optimal green light duration based on the queue length and average density, using a vehicle saturation flow departure model.

[0147] The timing strategy application unit is used to determine the theoretically optimal green light duration as the final green light duration in response to the current control mode being the local optimal mode, and to calculate the corrected green light duration as the final green light duration in response to the current control mode being the global safety mode.

[0148] This embodiment provides a detailed breakdown of the internal structure of the signal timing module, which includes:

[0149] The duration benchmark calculation unit is responsible for calculating the theoretical green light duration. This unit receives queue length and average density data from the state quantization module and calculates the green light duration based on this data. Departure model based on vehicle saturation flow Calculate ;

[0150] The timing strategy application unit is responsible for making the final decision or processing of the reference duration based on the control mode transmitted from the superior module; this unit receives the output from the duration reference calculation unit. In response to the command that the current control mode is ModeL, this unit directly... The final green light duration is determined; in response to the command that the current control mode is ModeG, this unit initiates its internal correction algorithm to calculate... As the final green light duration.

[0151] Example 9:

[0152] This system also includes:

[0153] The closed-loop adaptive correction module is used to compare the control mode switching index with the preset recovery threshold in response to the current control mode being the global safety mode, where the recovery threshold is greater than the switching threshold.

[0154] The closed-loop adaptive correction module is also used to switch the current control mode from the global safe mode to the local optimal mode in response to the control mode switching index being greater than the recovery threshold, and to maintain the current control mode as the global safe mode in response to the control mode switching index not being greater than the recovery threshold.

[0155] This embodiment adds a closed-loop adaptive correction module to the system described in Embodiment 6;

[0156] The function of this closed-loop adaptive correction module is to be activated under specific conditions when the system is in ModeG; after activation, the module will continuously monitor the values ​​calculated by the control switching evaluation module. and associate it with an internally set value greater than of Compare;

[0157] This closed-loop adaptive correction module is also used to execute specific mode switching commands; in response to It will issue a forced command to the control switching evaluation module, causing it to switch the current control mode from ModeG to ModeL; in response to It does not issue any instructions, thus allowing the control switching evaluation module to maintain the current control mode as ModeG.

[0158] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling mobile temporary traffic lights for traffic safety, characterized in that, include: Real-time data collection of queue length and average density of waiting lanes upstream of traffic lights, and acquisition of average vehicle speed and road saturation downstream of traffic lights; The upstream passage demand index is calculated based on the collected queue length and average density. Calculate the downstream congestion risk index based on average vehicle speed and road saturation; The control mode switching index is calculated by combining the upstream traffic demand index and the downstream congestion risk index. The control mode switching index is compared with a preset switching threshold to determine the current control mode; Based on the determined current control mode, generate the final green light duration; Output the control command corresponding to the final green light duration; The final green light duration includes: Based on queue length and average density, calculate the estimated number of vehicles in the queue; Based on the estimated number of vehicles, the theoretically optimal green light duration is calculated using a vehicle saturation flow departure model. In response to the current control mode being the local optimal mode, the theoretically optimal green light duration is determined as the final green light duration; In response to the current control mode being global safety mode, the corrected green light duration is calculated and determined as the final green light duration; The calculated corrected green light duration includes: The difference between the theoretically optimal green light duration and the preset minimum safe green light duration is determined as the compressible elastic duration. Calculate the attenuation factor based on the downstream congestion risk index; Multiply the compressible elastic duration by the attenuation factor to obtain the corrected elastic duration; The corrected green light duration is generated by adding the adjusted flexible duration to the minimum safe green light duration.

2. The method for controlling a mobile temporary traffic light for traffic safety according to claim 1, characterized in that, The current control mode includes: If the control mode switching index is greater than the switching threshold, the current control mode is determined to be the local optimal mode. If the control mode switching index is not greater than the switching threshold, the current control mode is determined to be the global safety mode.

3. The method for controlling a mobile temporary traffic light for traffic safety according to claim 1, characterized in that, Also includes: In response to the current control mode being global safety mode, the control mode switching index is compared with a preset recovery threshold, wherein the recovery threshold is greater than the switching threshold; When the control mode switching index exceeds the recovery threshold, the current control mode is switched from the global safe mode to the local optimal mode. If the control mode switching index is not greater than the recovery threshold, the current control mode is maintained as the global safety mode.

4. A mobile temporary traffic light control system for traffic safety, based on the mobile temporary traffic light control method for traffic safety according to any one of claims 1-3, characterized in that, include: The traffic parameter acquisition module is used to collect the queue length and average density of the waiting lane upstream of the traffic light in real time, and to obtain the average vehicle speed and road saturation of the road section downstream of the traffic light. The state quantification module is used to calculate the upstream traffic demand index based on the collected queue length and average density, and to calculate the downstream congestion risk index based on the average vehicle speed and road saturation. The control mode switching assessment module is used to combine the upstream traffic demand index and the downstream congestion risk index to calculate the control mode switching index, and compare the control mode switching index with the preset switching threshold to determine the current control mode. The signal timing module is used to generate the final green light duration based on the determined current control mode and output the corresponding control command.

5. A mobile temporary traffic light control system for traffic safety according to claim 4, characterized in that, The control switching evaluation module is used for: If the control mode switching index is greater than the switching threshold, the current control mode is determined to be the local optimal mode. If the control mode switching index is not greater than the switching threshold, the current control mode is determined to be the global safety mode.

6. A mobile temporary traffic light control system for traffic safety according to claim 4, characterized in that, The signal timing module includes: The time benchmark calculation unit is used to calculate the theoretically optimal green light duration based on the queue length and average density, using a vehicle saturation flow departure model. The timing strategy application unit is used to determine the theoretically optimal green light duration as the final green light duration in response to the current control mode being the local optimal mode, and to calculate the corrected green light duration as the final green light duration in response to the current control mode being the global safety mode.

7. A mobile temporary traffic light control system for traffic safety according to claim 4, characterized in that, Also includes: The closed-loop adaptive correction module is used to compare the control mode switching index with the preset recovery threshold in response to the current control mode being the global safety mode, where the recovery threshold is greater than the switching threshold. The closed-loop adaptive correction module is also used to switch the current control mode from the global safe mode to the local optimal mode in response to the control mode switching index being greater than the recovery threshold, and to maintain the current control mode as the global safe mode in response to the control mode switching index not being greater than the recovery threshold.

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