A method for dynamic coordination optimization of urban arterial multi-path signal timing and guided vehicle speed considering overlapping phases

Abstract

The application provides a kind of urban trunk multi-path signal timing and guiding vehicle speed dynamic coordination optimization method considering lap phase, comprising: collecting road network geometry data and traffic flow data;Current cycle length and current control time length are increased by 1s, update traffic flow data;If the current cycle length has reached the cycle length, the intersection level signal control optimization model is constructed with the minimum intersection delay value as the target, and the optimal signal timing is obtained;If the current control time length has reached the control time length, the trunk level signal control and guiding vehicle speed optimization model is constructed with the maximum weighted green wave bandwidth of all key paths as the target, the optimal signal cycle and the maximum green wave bandwidth are obtained, and the optimized key path phase difference and guiding vehicle speed are output.The signal timing and guiding vehicle speed coordination optimization scheme generated by the application can ensure that vehicles can still smoothly pass through multiple intersections under fluctuation, realize the coordination optimization of traffic signal and vehicle speed control, improve the operation efficiency of traffic flow, and reduce waiting time and parking times.

Description

Technical Field

[0001] This invention relates to the field of urban road traffic signal control, specifically but not limited to a method for dynamic coordination and optimization of multi-path signal timing and guiding vehicle speed on urban trunk roads, taking into account overlapping phases. Background Technology

[0002] In recent years, with the upgrading of urban traffic data collection technology and the acceleration of information technology construction, more accurate traffic flow status information can be obtained, and signal timings at intersections can be continuously updated, and information can be proactively released to adjust the guidance speeds of various road segments. Urban arterial road coordinated control has long been considered one of the effective methods to reduce congestion and shorten travel time. However, current arterial road coordinated optimization strategies are often designed based on traditional data collection technologies. These strategies assume that the arrival rates and timings at each intersection are fixed, or only consider the two-way straight paths as critical paths and set fixed guidance speeds. However, in reality, the arrival rates at each intersection and the critical paths affecting traffic operation are constantly changing, and guidance speeds can be continuously adjusted based on information pushed by variable message signs and connected vehicles. This results in the underutilization of available data and feasible control measures. Current optimization control methods for arterial roads lack consideration for dynamic changes in path traffic flow and lack an overall framework for coordinated control at the arterial level and adaptive control at the intersection level.

[0003] Therefore, there is an urgent need for a method to coordinate dynamically variable segment guidance speeds with adaptive signal control systems based on real-time traffic demand data, thereby optimizing the overall operation of urban arterial traffic. Overlapping phases are a common method in intersection adaptive signal control, where one or more traffic flows in the corresponding phase of another loop are initiated or terminated before the end of a certain phase. Essentially, it is a fixed-phase-sequence loop-grid signal control system. By introducing adaptive control methods that consider overlapping phases into the timing calculations of single-point intersections, and optimizing phase differences and synchronously optimizing arterial guidance speeds at the arterial level, a dynamic coordination and optimization framework for arterial traffic can be established, enabling coordinated control of arterial signal timing and vehicles.

[0004] In view of this, a new method is needed to solve at least some of the above problems. Summary of the Invention

[0005] To address one or more problems in existing technologies, this invention proposes a dynamic coordination optimization method for multi-path signal timing and guiding vehicle speed on urban arterial roads, considering overlapping phases. By using road network geometric data and path traffic flow data, the method calculates vehicle speed fluctuations and sets speed fluctuation constraints to ensure vehicles can smoothly pass through multiple intersections even under fluctuating conditions. By constructing and solving an optimization model for signal control and guiding vehicle speed, the method minimizes delays at each intersection and maximizes the green wave bandwidth of each path, achieving coordinated optimization of traffic signals and vehicle speed control, improving traffic flow efficiency, and reducing waiting time and the number of stops.

[0006] The technical solution to achieve the purpose of this invention is as follows:

[0007] A method for dynamic coordination and optimization of multi-path signal timing and guiding speed on urban arterial roads considering overlapping phases includes the following steps:

[0008] S1. Collect traffic data, including road network geometry data and traffic flow data.

[0009] Step S1 includes the following sub-steps:

[0010] S101. Obtain road network geometry data: the number of approach lanes for each direction at each intersection, the location information of each intersection, and the distance d between adjacent intersections. k It is used to calculate the vehicle's travel time.

[0011] S102. Obtaining traffic flow data: Obtaining the traffic flow f at each intersection within a control time domain through real-time monitoring equipment. i Path traffic is obtained through Automatic Vehicle Identification (AVI) systems or other real-time monitoring facilities.

[0012] S2, Current period duration t c Increase by 1s:t c =t c +1, current control time domain duration t h Increase by 1s:t h =t h +1, t c With t h These are the current cycle duration and the current control time domain duration, respectively. After increasing by 1 second, S102 is executed again to update the traffic flow data information.

[0013] S3. Determine if the current cycle duration has reached a full cycle duration. If not, proceed directly to S4. If a full cycle duration has been reached, reset the current cycle duration, build the model, solve it, and then proceed to S4. Construct an intersection-level signal control optimization model. With the goal of minimizing intersection delay, under constraints of cycle time, phase duration, and the change in phase duration during the cycle, use dynamic programming to calculate the timing of each phase at the intersection level, obtaining the optimal duration for each phase, thus changing the number of stages and the duration of each stage at the intersection. This process allows for early opening and late closing of phases, and can result in phase overlap.

[0014] Step S3 includes the following sub-steps:

[0015] S301. If the current time reaches a cycle duration, proceed to the next sub-step.

[0016] S302. Establish and solve the signal control optimization model:

[0017] The intersection has 8 phases, distributed within a phase structure composed of rings and gates. The position of the gates is variable within a certain range, while the phase switching points can slide between the gates and the boundaries, allowing the phase sequence to remain unchanged while the phase duration changes, forming overlapping phases. The resulting number of phases is ≥4 and ≤6. The model objective function is:

[0018] min d k (j)

[0019] d k (j) represents the total intersection delay in the j-th cycle of intersection k, where j represents the number of cycles. The total intersection delay is calculated as follows:

[0020]

[0021] Q l,k (t,j) represents the queue length on lane k at intersection k in second t; L represents the total number of lanes at intersection k. Q l,k (t,j) is calculated based on the queue length, vehicle arrival rate, and departure rate of the previous second:

[0022]

[0023] μ l,k (t,j) represents the arrival rate of lane l at intersection k in the t-th second of the j-th cycle. l,k (t,j) represents the departure rate on lane l of intersection i at second t in the j-th cycle. l,k (t,j) is related to the signal light color of the phase corresponding to lane l. When the phase corresponding to the lane where the queue is located is green, the departure rate is s. l,k (j). s l,k(t,j) represents the saturation flow rate of lane l at intersection k in the t-th second of the j-th cycle. When the corresponding phase is red, the departure rate is 0.

[0024]

[0025] The constraints in the model established in step S302 above are as follows:

[0026] (1) Periodic time constraint:

[0027]

[0028] The above formula ensures that the sum of the effective green time and the lost time equals the cycle time, where p represents the phase; N represents the total number of phases in the loop; g k,p (j) represents the effective green light time of phase p at intersection k in the j-th cycle, in seconds. k,p (j) represents the time loss of phase p at intersection k in the j-th cycle, and c represents the duration of the cycle.

[0029] (2) Phase time length constraint:

[0030]

[0031] The above formula ensures that the green light time at each stage is between the minimum and maximum green light times, where the minimum and maximum green light times are represented by g. min and g max P represents the set of phases.

[0032] (3) The constraints on the variation of phase duration during the cycle are as follows:

[0033]

[0034] The above formula requires that the adjustment of the green light time between two adjacent cycles be within a certain range to ensure a stable transition of the green light time, where Δg i This indicates the maximum change in green light time between two consecutive cycles.

[0035] The aforementioned signal control optimization model is solved using dynamic programming. First, the upper layer iterates through the feasible grid positions, and the lower layer iterates through the positions of each feasible phase switching point in the dual-ring, obtaining the optimal phase switching point and minimum delay value for each grid position. Since there are 8 phases in total (including the dual-ring), and some phases exhibit early start and late stop, the number of stages obtained is greater than or equal to 4 and less than or equal to 6.

[0036] S4. Determine if the current control time domain duration has reached a certain control time domain duration. If it has not reached a certain control time domain duration, go to S2. If the current control time domain length is t...h If the preset control time domain duration is reached, then the current control time domain duration t is reset to zero. h Based on traffic flow data, critical paths on the trunk lines are determined. Traffic flows on these critical paths pass through multiple intersections, involving the timing of these intersections and the coordination of guiding speeds between intersections. By using appropriate timing and guiding speeds, vehicles can pass through multiple intersections without stopping, forming a "green wave." The length of the green light duration for which vehicles can pass without stopping is the green wave bandwidth. With the objective of maximizing the traffic flow-weighted sum of green wave bandwidths for all critical paths, a trunk-level signal control and guiding speed coordination optimization model is constructed. Through period integer constraints, interference constraints, guiding speed range constraints, and travel time constraints, the maximum green wave bandwidth is obtained, and the optimized critical path phase difference and guiding speed are output. These, along with the results from S3, form a complete timing and guiding speed scheme.

[0037] Step S4 includes the following sub-steps:

[0038] S401. Determine if the current control time domain duration has reached a certain control time domain duration. If it has, set it to 0 and proceed to the next sub-step.

[0039] S402. Select the top m paths with the highest traffic as critical paths and use them as the optimization targets for the model established in subsequent steps.

[0040] S403. Construct a signal control optimization model to optimize signal timing for critical paths. Specifically, the objective function is to maximize the weighted green wave bandwidth of all paths, as shown in the following equation:

[0041]

[0042] Among them, f i and These represent the traffic on the critical path i of the uplink and downlink paths, respectively. i and , i, represent the green wave bandwidths of the uplink and downlink critical paths i, respectively, and , which are the decision variables of the model.

[0043] By adding periodic integer constraints, interference constraints, guiding vehicle speed range constraints, and travel time constraints, and solving the signal control optimization model, the optimized signal period and green wave bandwidth are obtained, and the optimized signal timing strategy is output.

[0044] (1) Time period constraint:

[0045]

[0046] Where, θ k Let k be the phase difference at intersection k. Let i be the red light duration at intersection k for the upbound (downbound) path i. This represents the time it takes for the green light to disappear before the green wave appears at intersection k for the upbound (downbound) path i. The uphill (downhill) travel time between adjacent intersection k and intersection k+1. Let i be a periodic integer variable representing the path i in the uplink (downlink) direction at intersection k. Let be the time it takes for the queue of vehicles on the up (down) path i to clear at intersection k. I represents the set of all critical paths in the uplink (downlink) direction. i This represents the set of intersections corresponding to path i.

[0047] (2) Interference constraints:

[0048]

[0049] Based on the intersection-level solution obtained from S3, the green light time g of the uplink critical path i at intersection k can be obtained. i,k The green light time of the downlink critical path i at intersection k P represents the set of all critical paths in the uplink. Represents the set of all critical downlink paths; I i Let b represent the set of intersections corresponding to the critical path i. i and These are the green wave bandwidths for the uplink and downlink critical paths i, respectively.

[0050] (3) Guided vehicle speed range constraints:

[0051]

[0052] v min It is the minimum speed allowed on the road, v max These are the maximum permissible speeds on the road, both in meters per second. In this example, the values ​​are 7 and 13 respectively.

[0053] (4) Travel time constraints:

[0054]

[0055] d k This represents the distance between intersections k and k+1. It is the green wave guide speed for the uphill (downhill) road segment between intersection k and k+1, where c is the cycle length, which is 120s in this example.

[0056] Finally, through the above signal control optimization model, the optimized signal control parameters of each intersection in the trunk line are output, and the weighted green wave bandwidth value of each critical path is obtained.

[0057] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0058] 1. The present invention provides a dynamic coordination optimization method for multi-path signal timing and guiding vehicle speed on urban arterial roads that considers overlapping phases. By introducing overlapping phases, it overcomes the problem that most existing arterial road signal coordination control methods assume fixed timing at the intersection level and ignore traffic flow changes at the intersection level. This enables the intersection-level timing to adapt to traffic flow changes, quickly respond to fluctuating traffic flow, and improve the utilization efficiency of green wave zones.

[0059] 2. The present invention provides a dynamic coordination optimization method for multi-path signal timing and guiding vehicle speed on urban arterial roads that considers overlapping phases. It adopts dynamic multi-path signal timing coordination, which overcomes the problem that existing technologies only optimize signals for a fixed single path. It can determine the dynamically changing critical path based on real-time traffic data in complex arterial road traffic environments, and achieve coordinated optimization of traffic flow in multiple directions.

[0060] 3. The present invention provides a dynamic coordination optimization method for multi-path signal timing and guiding vehicle speed on urban trunk lines that considers overlapping phases. By setting the guiding vehicle speed as a decision variable, it provides a guiding speed associated with the green wave timing scheme, effectively improving the effect of green wave control. It solves the optimal phase difference and optimal driving speed on road segments between multiple intersections on trunk lines, expands the bandwidth space, and avoids unnecessary stopping and waiting time. Attached Figure Description

[0061] The accompanying drawings are provided to further illustrate the invention and, together with the description, serve to explain embodiments of the invention, but do not constitute a limitation thereof. In the drawings:

[0062] Figure 1 This is a flowchart of the method for dynamic coordination and optimization of multi-path signal timing and guiding vehicle speed on urban trunk lines, taking into account overlapping phases, according to the present invention.

[0063] Figure 2 This is a schematic diagram showing the positional relationship and distance of each intersection according to an embodiment of the present invention.

[0064] Figure 3 These are traffic flow diagrams for each intersection according to an embodiment of the present invention: (a) is the traffic flow diagram for intersection 1, (b) is the traffic flow diagram for intersection 2, (c) is the traffic flow diagram for intersection 3, and (d) is the traffic flow diagram for intersection 4.

[0065] Figure 4 This is a signal timing diagram for each intersection according to an embodiment of the present invention.

[0066] Figure 5 This is a schematic diagram of the phase ring gate structure according to an embodiment of the present invention. Detailed Implementation

[0067] To further understand the present invention, preferred embodiments of the present invention are described below in conjunction with examples. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, and not for limiting the scope of the claims of the present invention.

[0068] The description in this section pertains only to typical embodiments, and the present invention is not limited to the scope of the embodiments described. Combinations of different embodiments, substitution of some technical features in different embodiments, and substitution of similar or identical prior art with some technical features in the embodiments are also within the scope of the description and protection of the present invention.

[0069] To verify the effectiveness of this invention, a section of main road was selected as a test case. This section contains four signalized intersections and involves traffic flows along three critical paths, such as... Figure 3 As shown, due to the uneven distribution of traffic arrival time at intersections and the large variation of path traffic over time, fixed signal timing can easily cause congestion.

[0070] A method for dynamic coordination and optimization of multi-path signal timing and guiding speed on urban arterial roads considering overlapping phases, such as... Figure 1 As shown, it includes the following steps:

[0071] S1. Collect traffic data, including road network geometry data and traffic flow data.

[0072] Step S1 includes the following sub-steps:

[0073] S101. Obtain road network geometry data: the number of approach lanes for each direction at each intersection, the location information of each intersection, and the distance d between adjacent intersections. k Used to calculate vehicle travel time, such as Figure 2 The diagram shows the location information and distance of the intersection.

[0074] S102. Obtaining traffic flow data: Obtaining the traffic flow f at each intersection within a control time domain through real-time monitoring equipment. i ,like Figure 3 As shown, (a) is the traffic flow map for intersection 1, (b) is the traffic flow map for intersection 2, (c) is the traffic flow map for intersection 3, and (d) is the traffic flow map for intersection 4. Path traffic was obtained through an Automatic Vehicle Identification (AVI) system or other real-time monitoring facilities. Table 1 shows the traffic flow for some paths.

[0075] Table 1 Traffic flow of the route to be optimized

[0076]

[0077] S2, Current period duration t c Increase by 1s:t c =t c +1, current control time domain duration t h Increase by 1s:t h =t h +1, t c With t h These are the current cycle duration and the current control time domain duration, respectively. After increasing by 1 second, S102 is executed again to update the traffic flow data information.

[0078] S3. Determine if the current cycle duration has reached a full cycle duration. If not, proceed directly to S4. If a full cycle duration has been reached, reset the current cycle duration, establish the model, solve it, and then proceed to S4. Construct an intersection-level signal control optimization model. With the goal of minimizing intersection delay, and under constraints of cycle time, phase duration, and the change in phase duration during the cycle, use dynamic programming to calculate the timing of each phase at the intersection level.

[0079] Step S3 includes the following sub-steps:

[0080] S301, The current time has reached a cycle duration of 120s, proceed to the next sub-step.

[0081] S302. Establish and solve the signal control optimization model:

[0082] The intersection has a total of 8 phases, distributed in a phase structure composed of rings and grids, such as... Figure 4 As shown. The position of the gate can vary within a certain range, while the phase switching point can slide between the gate and the boundary, so that the phase sequence remains unchanged while the phase duration changes, forming overlapping phases. The resulting number of stages is ≥4 and ≤6. The model objective function is:

[0083] min d k (j)

[0084] d k (j) represents the total intersection delay in the j-th cycle of intersection k, where j represents the number of cycles. The total intersection delay is calculated as follows:

[0085]

[0086] Q l,k (t,j) represents the queue length on lane k at intersection k in second t; L represents the total number of lanes at intersection k. In this example, all approach lanes have 3 lanes, corresponding to left turn, straight ahead, and right turn respectively. Q l,k (t,j) is calculated based on the queue length, vehicle arrival rate, and departure rate of the previous second:

[0087]

[0088] μ l,k (t,j) represents the arrival rate of lane l at intersection k in the t-th second of the j-th cycle. In this example, the traffic flow of the previous 5 cycles is used for calculation. l,k (t,j) represents the departure rate on lane l of intersection i at second t in the j-th cycle. l,k (t,j) is related to the light color corresponding to the phase. When the phase corresponding to the lane where the queue is located is green, the departure rate is the saturation flow rate s. l,k (j). When the corresponding phase is red, the departure rate is 0:

[0089]

[0090] In this example, s l,k (j) Take 0.47veh / s.

[0091] The constraints in the model established in step S302 above are as follows:

[0092] (1) Periodic time constraint:

[0093]

[0094] The above formula ensures that the sum of the effective green time and the lost time equals the cycle time, where p represents the phase; N represents the total number of phases in the loop; g k,p (j) represents the effective green light time of phase p at intersection k in the j-th cycle, in seconds. k,p (j) represents the time loss of phase p at intersection k in the j-th cycle, which is set to 3s in this example. c represents the duration of the cycle, which is set to 120s.

[0095] (2) Phase time length constraint:

[0096]

[0097] The above formula ensures that the green light time at each stage is between the minimum and maximum green light times, where the minimum and maximum green light times are represented by g. min and g max In this example, we take g. min =5s,g max =90s, P represents the set of phases.

[0098] (3) Constraints on the variation of phase duration during the cycle:

[0099]

[0100] The above formula requires that the adjustment of the green light time between two adjacent cycles be within a certain range to ensure a stable transition of the green light time, where Δg i This represents the maximum change in green light time between two consecutive cycles. In this example, the value is 20 seconds.

[0101] The aforementioned signal control optimization model is solved using dynamic programming. First, the upper layer iterates through the feasible grid positions, and the lower layer iterates through the positions of each feasible phase switching point in the dual-loop system, obtaining the optimal phase switching point and minimum delay value for each grid position. Since there are 8 phases in total (including the dual-loop system), and some phases exhibit early start and late stop, the number of stages obtained is greater than or equal to 4 and less than or equal to 6. The timing results for each stage are shown below. Figure 5 .

[0102] S4. Determine if the current control time domain duration has reached a certain control time domain duration. If not, go to S2. If it has reached a certain control time domain duration, reset the accumulated duration to zero. Construct a trunk line signal control and guidance speed optimization model: With the goal of maximizing the weighted green wave bandwidth of all paths, construct an objective function, and add period integer constraints, interference constraints, guidance speed range constraints, and travel time constraints. Solve the signal control optimization model to obtain the optimized signal period and green wave bandwidth, and output the optimized signal timing and guidance speed strategy.

[0103] Step S4 includes the following sub-steps:

[0104] S401. Determine if the current control time domain duration has reached a certain control time domain duration. If the current control time domain duration exceeds 600s, set it to 0 and proceed to the next sub-step.

[0105] S402. Select the top m paths with the highest traffic as the critical paths, and use them as the optimization objectives for the model built in subsequent steps. In this example, m is set to 3. The critical paths in this example are as follows: Figure 5 As shown.

[0106] S403. Construct a signal control optimization model to optimize signal timing for critical paths. Specifically, the objective function is to maximize the weighted green wave bandwidth of all paths, as shown in the following equation:

[0107]

[0108] Among them, f i and These represent the traffic on the critical path i of the uplink and downlink paths, respectively. i and , i, represent the green wave bandwidths of the uplink and downlink critical paths i, respectively, and , which are the decision variables of the model.

[0109] By adding periodic integer constraints, interference constraints, guiding vehicle speed range constraints, and travel time constraints, and solving the signal control optimization model, the optimized signal period and green wave bandwidth are obtained, and the optimized signal timing strategy is output.

[0110] (1) Time period constraint:

[0111]

[0112] Where, θ k Let k be the phase difference at intersection k. Let i be the red light duration at intersection k for the upbound (downbound) path i. This represents the time it takes for the green light to disappear before the green wave appears at intersection k for the upbound (downbound) path i. The uphill (downhill) travel time between adjacent intersection k and intersection k+1. Let i be a periodic integer variable representing the path i in the uplink (downlink) direction at intersection k. Let be the time it takes for the queue of vehicles on the up (down) path i to clear at intersection k. Let I represent the set of all critical paths in the uplink (downlink) direction, and let I represent the set of intersections corresponding to path i.

[0113] (2) Interference constraints:

[0114]

[0115] Based on the intersection-level solution obtained from S3, the green light time of the uplink (downlink) critical path i at intersection k can be obtained. The green wave bandwidth is the critical path i for the uplink (downlink).

[0116] (3) Guided vehicle speed range constraints:

[0117]

[0118]

[0119] v min It is the minimum speed allowed on the road, v max It is the maximum speed allowed on the road. This represents the green wave guidance speed for the uphill (downhill) traffic segment between intersections k and k+1, both measured in meters per second. In this example, the values ​​are 7 and 13 respectively.

[0120] (4) Travel time constraints:

[0121]

[0122] d kThis represents the distance between intersections k and k+1, and c is the cycle length, which is 120s in this example.

[0123] Finally, using the above signal control optimization model, the optimized signal control parameters for each intersection in the trunk line are output, and the weighted green wave bandwidth values ​​for each critical path are obtained as shown in Table 2:

[0124] Table 2

[0125]

[0126] The calculation results of the guiding vehicle speed for the road segment are shown in Table 3:

[0127] Table 3

[0128]

[0129] When issuing speed guidance, the speed is approximated and converted into speed units that are easy for drivers to understand: 13 m / s is converted to 45 km / h, 7 m / s is converted to 25 km / h, and 10 m / s is converted to 35 km / h.

[0130] This embodiment demonstrates that, by using the method of the present invention, vehicles can implement adaptive signal timing control at the intersection level that takes into account overlapping phases, thereby generating a larger green wave bandwidth under variable guide speeds to cope with dynamically changing traffic flow.

[0131] Simulation studies of this example have verified the superiority of the present invention on urban signal control trunk lines. In particular, when considering overlapping phases to achieve adaptive timing and variable guidance speed at intersections, the method can significantly improve traffic operation efficiency.

[0132] The description and application of the present invention herein are illustrative and not intended to limit the scope of the invention to the embodiments described above. The effects or advantages described in the specification may not be apparent in actual experimental cases due to uncertainties in specific conditions or other factors, and such descriptions are not intended to limit the scope of the invention. Variations and modifications to the embodiments disclosed herein are possible, and various substitutions and equivalents of the components in the embodiments are well known to those skilled in the art. Other variations and modifications can be made to the embodiments disclosed herein without departing from the scope and spirit of the invention.

Claims

1. A method for dynamic coordination optimization of urban arterial multi-path signal timing and guidance speed considering the overlap phase, characterized in that, include: S1. Collect road network geometric data and traffic flow data; S2, Set the current cycle duration Increase by 1 second, current control time domain duration Increase by 1 second and update the traffic flow data, where c represents the current period and h represents the current control time domain; S3, judging the current cycle length whether the preset cycle length is reached: If the current cycle length has not reached the preset cycle length, go to S4. If the current cycle duration If the preset cycle duration has been reached, the current cycle duration will be reset to zero. With the goal of minimizing intersection delay, an intersection-level signal control optimization model is constructed. Through period time constraints, phase duration constraints, and phase duration period variation constraints, the optimal signal timing for each phase at the intersection level under overlapping phases is obtained. S4. Determine the current control time domain length. Has the preset control time range been reached? If the current control time domain length S2, if the preset control time domain length is not reached. If the current control time domain length If the preset control time domain duration is reached, the current control time domain duration is reset to zero. Based on traffic flow data, critical paths on urban trunk lines are determined. With the goal of maximizing the sum of traffic flow-weighted green wave bandwidths of all critical paths, a trunk line-level signal control and guidance speed coordination optimization model is constructed. Through period integer constraints, interference constraints, guidance speed range constraints, and travel time constraints, the maximum green wave bandwidth and the optimal critical path phase difference and its guidance speed are obtained. This, together with the optimal signal timing, forms a complete optimal scheme for signal timing and guidance speed. The green wave bandwidth refers to the length of the green light time range during which vehicles can pass without stopping. The periodic integer constraint is: ， ， in, Intersection phase difference, , Upward or downward path respectively At the intersection Red light duration at the location , Upward or downward path respectively At the intersection The time it takes for the green light to disappear before the green wave appears. , Adjacent intersections to the intersection The travel time between the uphill and downhill sections. , Upward or downward path respectively At the intersection Periodic integer variables at the location, , Upward or downward path respectively The convoy at the intersection The queue clearing time at the location , This represents the set of all critical paths, either up or down. Representing a path The corresponding set of intersections; The interference constraint is: ， ， in, , Up and down paths At the intersection The green light time at the location , Key paths for upward and downward movement Green wave bandwidth; The constraint on the guided vehicle speed range is as follows: ， ， in, It is the minimum speed allowed on the road. It is the maximum speed allowed on the road. , These are the guiding speeds for the upbound or downbound traffic on the road segment between intersection k and k+1, respectively. The travel time constraint is as follows: ， ， Indicates an intersection and The distance between them The period length is denoted as .

2. The method for dynamic coordination and optimization of multi-path signal timing and guiding vehicle speed on urban trunk lines considering overlapping phases, as described in claim 1, is characterized in that... In S1: The road network geometry data includes the number of approach lanes for each direction at each intersection, the location of each intersection, and the distance between adjacent intersections. , k represents the kth intersection, used to calculate the vehicle's travel time; The traffic flow data includes the arrival rates of each approach lane at each intersection. And queue length, flow rate of each path and signal control data, Indicates the first There are 1 inlet channel, where i represents the i-th path, used to determine the critical path and calculate the bandwidth weight corresponding to each critical path.

3. The method for dynamic coordination and optimization of multi-path signal timing and guiding vehicle speed on urban trunk lines considering overlapping phases, as described in claim 1, is characterized in that... The intersection-level signal control optimization model described in S3 is as follows: ， ， ， ， in, Indicates the first Intersection of each cycle Total intersection delays Indicates the number of cycles; Indicates the first The first cycle Intersection within seconds lane queue length, Indicates the first The total number of lanes at each intersection; Indicates the first The first cycle Intersection within seconds lane Arrival rate, Indicates the first The first cycle Second Crossroad lane departure rate The color of the traffic light is related to the phase of the current lane. Indicates the first The first cycle Intersection within seconds lane saturation flow rate.

4. The method for dynamic coordination and optimization of multi-path signal timing and guiding vehicle speed on urban trunk lines considering overlapping phases, as described in claim 1 or 3, is characterized in that... In S3: The periodic time constraint is: ， in, Indicates phase, Indicates the total number of stages. Indicates the first Intersection in each cycle phase The effective green light time Indicates the first Intersection in each cycle phase The lost time, Indicates the duration of the period; The phase duration constraint is as follows: ， in, Indicates the first The minimum green light time in each cycle. Indicates the first The maximum green light time in a cycle. A set representing phases; The constraint on the variation of the phase duration during the cycle is: ， in, This indicates the maximum change in green light time between two consecutive cycles.

5. The method for dynamic coordination and optimization of multi-path signal timing and guiding vehicle speed on urban trunk lines considering overlapping phases, as described in claim 1, is characterized in that... In S3, dynamic programming is used to solve the intersection-level signal control optimization model and output the timing of each phase at the intersection level. The timing of the phase follows the characteristics of the ring grid structure. The specific solution includes: the upper layer traversing the positions of feasible grids, and the lower layer traversing the positions of each feasible phase switching point of the double ring to obtain the optimal phase switching point and minimum delay value corresponding to each grid position.

6. The method for dynamic coordination and optimization of multi-path signal timing and guiding vehicle speed on urban trunk lines considering overlapping phases, as described in claim 1, is characterized in that... In S4, the top m paths with the highest traffic are selected as the critical paths to be optimized.

7. The method for dynamic coordination and optimization of multi-path signal timing and guiding vehicle speed on urban trunk lines considering overlapping phases, as described in claim 1, is characterized in that... The trunk-level signal control and guidance speed coordination optimization model described in S4 is as follows: ， in, Let i be the traffic of the uplink path i. Let i be the traffic of the downlink path i. Let be the bandwidth of the uplink path i. Let be the bandwidth of downlink path i.

8. The method for dynamic coordination and optimization of multi-path signal timing and guiding vehicle speed on urban trunk lines considering overlapping phases, as described in claim 1, is characterized in that... In S4, a mixed-integer linear programming method is used to solve the trunk-level signal control and guidance speed optimization model, calculate the optimal signal period and maximum green wave bandwidth, and output the optimal intersection signal timing and road segment guidance speed scheme.

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