A coordinated control method for intersection signal with variable phase sequence overflow based on MPC

Abstract

The present invention discloses a signal coordination control method for a variable phase sequence overflow intersection based on MPC, comprising: predicting the number of vehicles arriving at the overflow intersection, i.e., the stop line at the downstream intersection, in the future based on driving data of vehicles at the upstream intersection; calculating the queue length of vehicles at the downstream intersection based on the predicted number of arriving vehicles; constructing a signal timing optimization model for the downstream intersection based on dynamic programming, and solving the signal timing plan for the current optimization; and using the green light end time of the first non-zero green light duration in the previously optimized signal timing plan as the start time for the next optimization, thereby achieving dynamic rolling optimization control. The present invention uses vehicle trajectory big data collected at the upstream entrance of the intersection to estimate the arrival of downstream vehicles, construct a dynamic programming model to recommend the optimal signal timing plan for the overflow intersection, and thus achieve anti-overflow control at the intersection, thereby improving the efficiency of urban road traffic operation and reducing travel delays.

Description

Technical Field

[0001] The invention relates to an MPC-based variable phase sequence overflow intersection signal coordination control method, belonging to the technical field of traffic signal optimization. Background Art

[0002] Urban road traffic congestion often stems from high traffic demand in localized areas, which can easily lead to traffic overflow in certain areas. This occurs when vehicle queues accumulate on a road section, exceeding the length of the road section and overflowing into upstream intersections. Without effective control, overflow can quickly spread from a single road section or intersection to multiple related sections or intersections, even disrupting the normal operation of the entire road network. Ultimately, intersections become "locked" with each other, paralyzing traffic throughout the urban area. Signal control, as the most direct and easy-to-implement method for regulating supply and demand at intersections, can play a role in regulating overflow caused by oversaturation.

[0003] Traditional signalized intersections on urban roads often use historical road data as a basis for fixed or time-segmented signal timing plans for intersection lights. However, due to the time-varying and random nature of urban road traffic, historical data fails to accurately describe the distribution of current traffic flows. The resulting signal timing plans can significantly impact travel time and increase travel costs. In recent years, advances in technologies such as connected vehicles and video detection have enabled real-time acquisition of vehicle ID, speed, location, and OD information within a certain range of an intersection. This has opened up new opportunities for accurately predicting the movement and arrival status of road traffic.

[0004] Traffic systems are organized, directed, and controlled using various control hardware and software devices, such as traffic signals, computers, and variable signs. This ensures balanced temporal and spatial distribution of traffic flows and evens out traffic loads, enabling rational traffic control and management, improving the effectiveness of road traffic resource supply, and alleviating traffic pressure. Currently, signal timing cycles at urban intersections are mostly fixed in duration, and the phase sequence is fixed during the timing process. This results in inflexible control schemes, making it difficult to cope with time-varying traffic flows and causing unnecessary delays. For intersections with overflow, current control schemes often monitor vehicle queue data and switch the intersection's signal phase when overflow occurs. While this can control overflow, it struggles to improve the overall operational efficiency of the intersection. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a signal coordination control method for an MPC-based variable phase sequence overflow intersection. By utilizing the big data of vehicle trajectories at the overflow intersection, the arrival status of downstream vehicles is inferred through the driving status of upstream vehicles, and a dynamic optimization timing scheme with variable phase sequence and cycle is established to adapt to traffic flows under various conditions, improve the traffic efficiency of the intersection, and realize overflow control of the intersection.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] A method for coordinated control of signal at an intersection with variable phase sequence overflow based on MPC, comprising the following steps:

[0008] Step 1: Based on the driving data of vehicles at the upstream intersection, the number of vehicles that will arrive at the overflow intersection, i.e., the stop line at the downstream intersection, is predicted in the future;

[0009] Step 2: Calculate the queue length of the downstream intersection based on the number of vehicles arriving at the downstream intersection stop line predicted in step 1;

[0010] Step 3: Based on step 2, a dynamic programming-based signal timing optimization model for the downstream intersection is constructed, and forward recursion and backward recursion algorithms are used to solve the problem to obtain the current optimized signal timing plan.

[0011] Step 4: The green light end time of the first stage where the green light time length is not 0 in the last optimized signal timing plan is used as the start time of the next optimization to achieve dynamic rolling optimization control.

[0012] As a preferred embodiment of the present invention, the specific process of step 1 is as follows:

[0013] The Robertson fleet discrete model is used to estimate the number of vehicles arriving at the stop line of the downstream intersection at each future moment:

[0014] q D (t) = Fq U (tt d )+(1-F)q D (t-1)

[0015] Where q D (t), q D (t-1) are the number of vehicles that arrive at the stop line within the downstream intersection time interval t and t-1, q U (tt d ) is the upstream intersection time interval tt d Number of vehicles leaving the area, t d It is 0.8 times the average vehicle travel time between upstream and downstream intersection sections, and F is the fleet dispersion coefficient;

[0016] The vehicle speed is obtained in real time, and the rolling time window concept is used to dynamically calculate the fleet dispersion coefficient F corresponding to each cycle when the vehicle just enters the entrance of the downstream intersection:

[0017]

[0018]

[0019] T α =βμ

[0020]

[0021] Where α is the fleet dispersion parameter, β is the travel time coefficient, μ and σ represent the mean and standard deviation of the travel time between the upstream intersection and the stop line at the entrance of the downstream intersection, respectively.

[0022] As a preferred embodiment of the present invention, the specific process of step 2 is as follows:

[0023] Define several lanes with the same driving direction on the same entrance road as a lane group. Suppose there are N lane groups in the downstream intersection, and the nth lane group has m n lanes, n=1,…,N, m of the nth lane group at time t n The average number of vehicles queuing in a lane is the queue length L n,t ,Right now:

[0024]

[0025] Where, L n,t-1 A represents the queue length of vehicles in the nth lane group at time t-1, n,t represents the total number of vehicles arriving at the nth lane group at time t, D n,t represents the total number of vehicles leaving the nth lane group at time t, m n is the number of lanes in the nth lane group. When t=1, L n,t-1 That is, the queue length of vehicles in the nth lane group at the initial moment.

[0026] As a preferred solution of the present invention, in step 3, the downstream intersection signal timing optimization model based on dynamic programming is specifically:

[0027] min(maxL t )

[0028]

[0029] Where, L 1,t , L 2,t , LN,t They represent the queue lengths of vehicles in the 1st, 2nd, and Nth lane groups at time t respectively;

[0030] State variable s j Indicates the total time of the phases assigned after the end of stage j, s j The value range is: s j =r,…,T, r is the yellow light or full red time, T is the optimization time length of the current optimization;

[0031] The relationship between the state variables of adjacent stages is as follows:

[0032] s j-1 =s j -h j (x j )

[0033]

[0034] Where s j-1 represents the state variable of stage j-1, x j is the control variable, x j represents the green light time of stage j; x j All possible values ​​of are:

[0035]

[0036] Where, X j (s j ) indicates that given s j All available control variables x j is a set of , γ is the minimum green light time.

[0037] As a preferred solution of the present invention, for the constructed signal timing optimization model, a forward recursive algorithm is first used to calculate the optimal objective function value, and then a backward recursive algorithm is used to obtain the control variable values ​​of each stage under the optimal objective function condition. The forward recursive algorithm is specifically as follows:

[0038] Step 1: Let j = 1, s j-1 =0,v j (0)=0;

[0039] Step 2: Traverse the state variable s j The possible values ​​of ;

[0040] Step 3: Determine the given state variable s j When the control variable x j The value set X j (s j );

[0041] Step 4: Calculate and for the set X j (s j ) find the x j (s j ) that minimizes v j and denote it as the optimal value f j (s j [[ID=​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

[0051] A computer device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the steps of the MPC-based variable phase sequence overflow intersection signal coordination control method are implemented.

[0052] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the MPC-based variable phase sequence overflow intersection signal coordination control method.

[0053] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0054] The present invention uses vehicle trajectory big data collected at the upstream entrance of the intersection to estimate the arrival of downstream vehicles, and ultimately constructs a dynamic programming model to recommend the optimal signal timing plan for the overflow intersection, thereby realizing anti-overflow control at the intersection, improving the efficiency of urban road traffic operation, and reducing the level of travel delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is an MPC model predictive control framework;

[0056] Figure 2 It is a relationship diagram between dynamic programming related variables;

[0057] Figure 3 It is a schematic diagram of the rolling optimization strategy. DETAILED DESCRIPTION

[0058] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be interpreted as limiting the present invention.

[0059] The present invention proposes a method for coordinated control of variable phase sequence overflow intersection signals based on model predictive control (MPC). Figure 1 As shown in Figure 1, MPC is a rolling horizon optimization strategy. At each sampling instant, it uses an optimization algorithm to solve a finite-horizon optimal control problem to obtain the control action for each step in the time domain. The current state of the system is used as the initial state of the optimal control problem. Taking into account factors such as disturbances and uncertainties in the prediction model, the optimal control sequence solved only applies the control action at the current time step to the control system. MPC mainly consists of three parts according to its working process:

[0060] 1) Prediction Model

[0061] The prediction model is the basis of MPC. Its function is to predict the response at future moments based on the historical information of the controlled object and the future system control input, and use it as the input parameter of the optimization model.

[0062] 2) Rolling Optimization

[0063] MPC is also known as rolling horizon control due to its rolling optimization mechanism. Rolling optimization involves solving the optimization model to obtain control actions at each sampling moment, based on the system behavior predicted by the prediction model within a finite future timeframe. The optimization model aims to optimize the performance indicators in the optimization model.

[0064] 3) Feedback correction

[0065] At each sampling moment, MPC uses an optimization algorithm to solve the optimal control problem within a finite time domain, generating a sequence of control actions within that time domain. At the next sampling moment, the actual output of the controlled object is obtained and fed back to the MPC controller. By comparing it with the predicted output, the error between the actual and predicted outputs is compensated and corrected, and then the optimal control calculation is performed.

[0066] This invention utilizes the big data of vehicle trajectories at overflow intersections, infers the arrival status of downstream vehicles through the driving status of upstream vehicles, establishes a dynamic optimization timing scheme with variable phase sequence and cycle, and implements dynamic rolling optimization control to adapt to traffic flows under various conditions, improve the traffic efficiency of intersections, and realize overflow control at intersections. The specific steps are as follows:

[0067] Step 1: Infer the arrival status of downstream vehicles based on upstream vehicle data

[0068] Based on the status data of each vehicle in the traffic flow, the vehicle IDs of different flow directions are extracted and the corresponding vehicle speeds are obtained. The Robertson fleet discrete model is used to estimate the number of vehicles arriving at the downstream stop line at each time in the future:

[0069] q D (t) = Fq U (tt d )+(1-F)q D (t-1)

[0070] Where q D (t), q D (t-1) are the number of vehicles that arrive at the stop line within the downstream intersection time interval t and t-1, q U (tt d ) is the upstream intersection time interval tt d Number of vehicles leaving the area, t d It is 0.8 times the average vehicle travel time between upstream and downstream intersection sections, and F is the fleet dispersion coefficient;

[0071] Using the rolling time window concept, the calibration formula for the fleet dispersion coefficient parameter corresponding to each cycle when the vehicle just enters the downstream entrance is dynamically calculated as follows:

[0072]

[0073]

[0074] T α =βμ

[0075]

[0076] Where α is the fleet dispersion parameter, β is the travel time coefficient, μ and σ represent the mean and standard deviation of the travel time between the upstream intersection and the stop line at the entrance of the downstream intersection, respectively, which are calculated based on the speed data of the real-time trajectory.

[0077] Step 2: Calculate the intersection queue length based on the predicted downstream vehicle arrival data

[0078] Determine the number of vehicles queuing for each time period and obtain the maximum number of vehicles in the queue. Using vehicle trajectory data, a queued vehicle is defined as one traveling at a speed of less than 3 m / s for three consecutive seconds. Use the maximum queue length to identify intersection overflow conditions.

[0079] Suppose there are N lane groups at an intersection. A lane group is a set of lanes set at the entrance of the intersection to facilitate capacity and service level analysis. One or several lanes with the same turning function at the same entrance are defined as a lane group.

[0080] Assume that the nth lane group has m n lanes, then the vector formed by the number of lanes in all lane groups is:

[0081] Traffic flow state vector: m of the nth lane group at time t n The average number of vehicles queuing in each lane is L n,t , then the vector formed by the average number of queued vehicles in all lane groups at time t is:

[0082] As vehicles arrive and leave, the queue length L of vehicles in lane group n is n,t for:

[0083]

[0084] Among them, A n,t represents the total number of vehicles arriving in lane group n at time t, D n,t represents the total number of vehicles leaving the n lane group at time t, mn is the number of lanes in lane group n. When t=1, L n,t-1 is the number of vehicles in the queue at the initial moment.

[0085] Step 3: Construct a single-point traffic control model for the downstream intersection based on dynamic programming

[0086] At a typical four-way intersection, without signal control for right-turn traffic, there are eight phases for left turns and through traffic in four directions. Intersection signal optimization considers flexible combinations of non-conflicting phases, including simultaneous through-and-left turns on all entrances, and simultaneous through-and-left turns in all opposite directions. The phase structure adopts a double-loop structure.

[0087] Intersections prone to overflow often have shorter distances between upstream and downstream intersections, shorter predictable arrival times for vehicles at downstream intersections, and shorter signal optimization time intervals. Traditional COP algorithms only consider optimization problems involving complete optimization phases, where the optimization result is the optimal number of complete phases and their corresponding durations. To avoid limiting the duration of phase green light durations due to the requirement for complete optimization phases, which could affect signal optimization, the control algorithm of the present invention assumes that the last phase of an optimization interval is an incomplete phase, and that the first phase of an optimization interval is an incomplete phase from the previous optimization interval.

[0088] (1) Control objectives for oversaturated intersections:

[0089] min(maxL t )

[0090] (2) State variables

[0091] State variable s j Indicates the total time of the phases assigned after the end of stage j, s j The value range of is:

[0092] s j =r,…,T

[0093] r represents the yellow light or full red time, and T represents the optimization time length of the current optimization;

[0094] like Figure 2 As shown in , the relationship between the state variables of adjacent stages is as follows:

[0095] s j-1 =s j -h j (x j )

[0096]

[0097] (3) Control variables

[0098] Control variable x j Denote the green light time of stage j, x j All possible values X of j (s j ) are as follows:

[0099]

[0100] γ represents the minimum green light time, x j can take 0 to ensure that the algorithm can generate any required phase sequence.

[0101] (4) After obtaining the value ranges of relevant key variables in the dynamic programming algorithm, the optimal objective function value can be obtained by forward recursion, and then the decision variable values of each stage under the optimal objective function can be obtained by backward recursion. The forward recursion algorithm is as follows:

[0102] Step 1: Let j = 1, s j-1 = 0, v j (0) = 0;

[0103] Step 2: Traverse the possible values of the state variable s j ; <X

[0104] Step 3: Determine the value set X j of the control variable x j when the given state variable s j (s j );

[0105] Step 4: Calculate and record the optimal value f j (s j , x j ) as the maximum queue length value at the intersection during stage j for the given s j and x j ; v j (s j *) as the maximum queue length value at the intersection from the initial moment to stage j;

[0106] Step 5: Judge whether s j has been traversed. If so, go to Step 6; otherwise, go to Step 2;

[0107] Step 6: If j < P, let j = j + 1 and go to Step 2; if j ≥ P and for all k, v j-k (T) = v j (T), k ≤ P - 1, where P is the total number of stages of the overflow intersection, then end the algorithm; otherwise, go to Step 2.

[0108] The backward recursive algorithm is as follows:

[0109] After the forward recursive algorithm optimizes the control target, the backward recursive algorithm is used to obtain the optimal signal timing strategy. Assume that J is the last stage of the forward recursive algorithm to calculate the optimized value, then

[0110] Step 7: Let j = J-P+1, and obtain

[0111] Step 8: Calculation

[0112] Step 9: If j>1, set j=j-1 and go to Step 7, otherwise end the algorithm.

[0113] (5) Dynamic rolling optimization control

[0114] Assume s k For the first stage of the current optimization interval where the green light time is not 0, then in stage s k When the green light time is expected to end, the real-time collected vehicle trajectory data, the real-time detected downstream intersection vehicle queue length and the current stage s k The signal phase of the downstream intersection is re-optimized, and the new optimization result is used to overwrite the previous signal timing plan to implement a rolling optimization strategy. Figure 3 Schematic diagram of rolling optimization strategy.

[0115] (6) Upstream intersection control strategy

[0116] Based on the queue length detected in the key direction of the downstream intersection, if the queue length exceeds the set threshold, that is, the upstream intersection is about to overflow, the straight direction signal of the upstream intersection entrance lane is set to red.

[0117] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the aforementioned MPC-based variable phase sequence overflow intersection signal coordination control method are implemented.

[0118] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the aforementioned MPC-based variable phase sequence overflow intersection signal coordination control method.

[0119] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0123] The above embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for coordinated control of variable phase sequence overflow intersection signals based on MPC, characterized in that: The steps include: Step 1: Based on the driving data of vehicles at the upstream intersection, the number of vehicles that will arrive at the overflow intersection, i.e., the stop line at the downstream intersection, is predicted in the future; Step 2: Calculate the queue length of the downstream intersection based on the number of vehicles arriving at the downstream intersection stop line predicted in step 1. Step 3: Based on step 2, a dynamic programming-based signal timing optimization model for the downstream intersection is constructed, and forward recursion and backward recursion algorithms are used to solve the problem to obtain the current optimized signal timing plan. The downstream intersection signal timing optimization model based on dynamic programming is specifically as follows: min(max L t ) Where, L 1,t 、L 2,t 、L N,t They represent the queue lengths of vehicles in the 1st, 2nd, and Nth lane groups at time t respectively; State variable s j Indicates the total time of the phases assigned after the end of stage j, s j The value range is: s j =r,…,T, r is the yellow light or full red time, T is the optimization time length of the current optimization; The relationship between the state variables of adjacent stages is as follows: s j-1 =s j -h j (x j ) Where s j-1 represents the state variable of stage j-1, x j is the control variable, x j represents the green light time of stage j; x j All possible values ​​of are: Where, X j (s j ) indicates that given s j All available control variables x j The set of , γ is the minimum green light time; For the constructed signal timing optimization model, the forward recursive algorithm is first used to calculate the optimal objective function value, and then the backward recursive algorithm is used to obtain the control variable values ​​of each stage under the optimal objective function condition. The forward recursive algorithm is as follows: Step 1: Let j = 1, s j-1 =0,v j (0)=0; Step 2: Traverse the state variable s j The possible values ​​of ; Step 3: Determine the given state variable s j When the control variable x j The value set X j (s j ); Step 4: Calculation And the set X j (s j ) makes v j (s j )The smallest x j Recorded as the optimal value f j (s j ,x j ) is a given s j and x j The maximum queue length at the intersection during phase j is v j (s j j, v j-1 (s j-1 ) are the maximum queue lengths at the intersection from the initial moment to stage j and j-1 respectively; Step 5: Determine whether s has been traversed j If yes, go to Step 6, otherwise go to Step 2; Step 6: If j < P, set j = j + 1 and go to Step 2; if j ≥ P and for all k, v j-k (T) = v j (T), where k ≤ P - 1 and P is the total number of stages of the overflow intersection, then end the algorithm; otherwise, go to Step 2; The backward recursive algorithm is as follows: Let J be the last stage of the forward recursive algorithm to calculate the optimal value, then Let j = J - P + 1; Step 7: According to the determined and Step 4 to obtain Step 8: Calculation Step 9: If j>1, set j=j-1 and go to Step 7, otherwise end the algorithm; Thus, the current optimized signal timing plan is obtained; Step 4: The green light end time of the first stage where the green light time length is not 0 in the last optimized signal timing plan is used as the start time of the next optimization to achieve dynamic rolling optimization control.

2. The MPC-based variable phase sequence overflow intersection signal coordination control method according to claim 1 is characterized in that: The specific process of step 1 is as follows: The Robertson fleet discrete model is used to estimate the number of vehicles arriving at the stop line of the downstream intersection at each future moment: q D (t)=Fq U (t-t d )+(1-F)q D (t-1) Where q D (t), q D (t-1) are the number of vehicles arriving at the stop line within the downstream intersection time interval t and t-1, respectively, U (tt d ) is the upstream intersection time interval tt d Number of vehicles leaving the area, t d It is 0.8 times the average vehicle travel time between the upstream and downstream intersection sections, and F is the fleet dispersion coefficient; The vehicle speed is obtained in real time, and the rolling time window concept is used to dynamically calculate the fleet dispersion coefficient F corresponding to each cycle when the vehicle just enters the entrance of the downstream intersection: T α =bm Where α is the fleet dispersion parameter, β is the travel time coefficient, μ and σ represent the mean and standard deviation of the travel time between the upstream intersection and the stop line at the entrance of the downstream intersection, respectively.

3. The MPC-based variable phase sequence overflow intersection signal coordination control method according to claim 1 is characterized in that: The specific process of step 2 is as follows: Define several lanes with the same driving direction on the same entrance road as a lane group. Suppose there are N lane groups in the downstream intersection, and the nth lane group has m n lanes, n=1,…,N, m of the nth lane group at time t n The average number of vehicles queuing in a lane is the queue length L n,t ,Right now: Where, L n,t-1 A represents the queue length of vehicles in the nth lane group at time t-1, n,t represents the total number of vehicles arriving at the nth lane group at time t, D n,r represents the total number of vehicles leaving the nth lane group at time t, m n is the number of lanes in the nth lane group. When t=1, L n,t-1 That is, the queue length of vehicles in the nth lane group at the initial moment.

4. The MPC-based variable phase sequence overflow intersection signal coordination control method according to claim 1 is characterized in that: Calculate the length of vehicle queues in each direction at the downstream intersection and set a threshold for each direction. If the length of the vehicle queue in a certain direction exceeds the set threshold, it means that overflow is about to occur at the upstream intersection in that direction, and the signal for the straight-through direction of the entrance lane in that direction of the upstream intersection is set to red.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the MPC-based variable phase sequence overflow intersection signal coordination control method as described in any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the MPC-based variable phase sequence overflow intersection signal coordination control method according to any one of claims 1 to 4 are implemented.

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