A method and system for multi-path coordinated control of urban road network considering multi-dimensional dynamic characteristics

Abstract

The application relates to the field of traffic planning and discloses a kind of urban road network multi-path coordination control method and system considering multi-dimensional dynamic characteristics, wherein the method comprises: obtaining traffic flow data, congestion index and bidirectional green wave bandwidth difference data of each path in the intersection of road network;According to the traffic flow data, congestion index and bidirectional green wave bandwidth difference data, the multi-dimensional dynamic weight of each path at the intersection is calculated;A target function is constructed with the goal of maximizing the total sum of weighted green wave bandwidth, and the multi-dimensional dynamic weight is embedded in the target function;A collaborative optimization system is constructed, which includes high weight path guarantee constraint, dynamic adaptability constraint and auxiliary constraint;Based on the target function and the collaborative optimization system, the optimal signal timing parameters are solved, and a multi-path coordination control scheme is generated.The application improves the length of green wave band, the continuity of green wave and the anti-interference ability of road network through high weight path guarantee constraint, dynamic adaptability constraint, flexible breakpoint constraint and the like.

Description

Technical Field

[0001] This invention relates to the field of transportation planning, and specifically to a method and system for multi-path coordinated control of urban road networks that considers multi-dimensional dynamic characteristics. Background Technology

[0002] Modern urban development is inseparable from the high-quality operation of urban transportation systems. With the progress of urbanization, urban commuter traffic flow exhibits significant dynamic characteristics in its spatiotemporal distribution, especially in urban residential and commercial road networks, where signal control of multiple routes faces severe challenges.

[0003] With the continuous growth in demand for motor vehicle travel, the complex spatial and temporal distribution characteristics of numerous traffic flow paths in the road network are becoming increasingly prominent. Achieving coordinated control of multi-path traffic flows and improving road network efficiency has become a crucial problem urgently needing to be solved in the field of traffic engineering. Green wave coordination control is an important method for optimizing intersection signal timing and improving the continuity of traffic on arterial roads and regional road networks. Over the years, it has evolved from bidirectional green wave control on arterial roads to bidirectional asymmetric optimization, road network green wave coordination control, and path green wave coordination control. However, there is still room for further research in areas such as adapting to dynamic traffic characteristics and accurately allocating multi-path resources. Summary of the Invention

[0004] To address the technical problems mentioned above, the present invention provides the following technical solution:

[0005] A multi-path coordinated control method for urban road networks considering multi-dimensional dynamic characteristics includes:

[0006] Obtain traffic flow data, congestion index, and bidirectional green wave bandwidth difference data for each path in the road network at intersections;

[0007] Based on traffic flow data, congestion index, and bidirectional green wave bandwidth difference data, calculate the multidimensional dynamic weight of each path at the intersection.

[0008] An objective function is constructed with the goal of maximizing the total weighted green wave bandwidth, and the multidimensional dynamic weights are embedded in the objective function.

[0009] Construct a collaborative optimization system that includes high-weight path guarantee constraints, dynamic adaptive constraints, and auxiliary constraints;

[0010] Based on the objective function and the cooperative optimization system, the optimal signal timing parameters are solved to generate a multi-path coordinated control scheme.

[0011] Preferably, the multidimensional dynamic weights include: traffic weight, congestion penalty weight, and symmetry compensation weight; the calculation formulas for traffic weight, congestion penalty weight, and symmetry compensation weight are as follows:

[0012] ,

[0013] ,

[0014] ,

[0015] in, , , These are respectively traffic weight, congestion penalty weight, and symmetry compensation weight; q i Let i be the traffic flow at intersection k on path i; Total flow across all paths and directions; This represents the current congestion index; The congestion threshold; , These represent the green wave bandwidths of uplink path i and downlink path j at intersection k, respectively.

[0016] The traffic weight, congestion penalty weight, and symmetry compensation weight are weighted and merged into a multi-dimensional dynamic weight:

[0017] ,

[0018] in, , , These are the green wave bandwidths at intersection k for uplink path i, downlink path j, and mixed path m, respectively. This is the weighting factor.

[0019] Preferably, the step of establishing the high-weight path guarantee constraint includes:

[0020] Adjusting the phase difference between adjacent intersections based on path-multidimensional dynamic weighting:

[0021] ,

[0022] ,

[0023] in, , Let k be the phase difference between intersections k and k+1. Intersection , The distance between them; , The optimal driving speeds for up path i and down path j; , Intersection The phase difference adjustment amount for uplink path i and downlink path j;

[0024] Additional green light time based on multidimensional dynamic weight allocation:

[0025] ,

[0026] ,

[0027] in, Let i be the green light time at intersection k for path i; The basic green light ratio for intersection k; The green light weight compensation coefficient for the uphill path i at intersection k; The signal cycle duration at the intersection; , These represent the maximum and minimum values ​​of the signal period duration, respectively.

[0028] High-weight path bandwidth guarantee constraints:

[0029] The path weights are converted into bandwidth guarantees using unit weight bandwidth increments, and directly correlated with the weighted terms of the objective function to ensure the minimum bandwidth requirements of high-weight paths, with the following constraints:

[0030] ,

[0031] in, The minimum bandwidth is the baseline. The minimum weight is the benchmark. This represents the bandwidth increment corresponding to a unit weight.

[0032] Preferably, the step of establishing the dynamic adaptive constraints includes:

[0033] Introducing dynamic compensation terms Correcting green wave transition conditions:

[0034] ,

[0035] ,

[0036] Establish the correlation equation between phase difference and dynamic compensation term:

[0037] ,

[0038] ,

[0039] in, , These are the uplink paths at intersections k and k+1, respectively. The total duration of the red light to the left of the green wave band. , These represent the total duration of the red light to the right of the green wave band for downlink path j at intersections k and k+1, respectively. , These represent the duration of the green light before and after the green wave band for the uphill path i at intersections k and k+1, respectively. , These represent the duration of the green light before and after the green wave band for the downlink path j at intersections k and k+1, respectively. The uphill path at intersection k The number of signal cycles; The downlink path at intersection k The number of signal cycles; Uplink path The travel time between intersections k and k+1 Intersection Path Upward queuing release time; The downlink path at intersection k Queue release time The uphill path at intersection k Dynamic compensation items; This is the dynamic compensation term for the downlink path j at intersection k; This represents the green wave bandwidth of uplink path i at intersection k; This represents the green wave bandwidth of downlink path j at intersection k.

[0040] Preferably, the step of establishing the dynamic adaptive constraints further includes:

[0041] Constructing flexible constraints for travel time:

[0042] ,

[0043] ,

[0044] ,

[0045] ,

[0046] in, and These are the maximum and minimum speeds of the uphill path i, respectively; and These are the maximum and minimum travel speeds for downlink path j, respectively. and These are the slack variables for uplink path i and downlink path j between intersection k and k+1, respectively. It is the reciprocal of the intersection period; , Represents the path-intersection association set; Represents the set of uplink paths; This represents the set of downlink paths.

[0047] Preferably, the steps for constructing the auxiliary constraints are as follows:

[0048] ,

[0049] ,

[0050] In the formula, For set Size; For set Size; and These represent the allowed number of breakpoints for the uplink and downlink paths, respectively.

[0051] Preferably, the step of solving the optimal signal timing parameters includes: using a mathematical programming solver to solve a mixed integer linear programming model containing the objective function and the co-optimization system, and outputting the signal period, phase difference and green ratio parameters of each intersection.

[0052] The present invention also proposes a multi-path coordinated control system for urban road networks that considers multi-dimensional dynamic characteristics. The system is used to implement the above method and includes: a data acquisition module, a calculation module, an embedding module, a construction module, and a generation module.

[0053] The acquisition module is used to acquire traffic flow data, congestion index, and bidirectional green wave bandwidth difference data for each path in the road network at intersections.

[0054] The calculation module is used to calculate the multidimensional dynamic weight of each path at the intersection based on traffic flow data, congestion index and bidirectional green wave bandwidth difference data.

[0055] The embedding module is used to construct an objective function with the goal of maximizing the total weighted green wave bandwidth, and the multidimensional dynamic weights are embedded in the objective function.

[0056] The construction module is used to build a collaborative optimization system that includes high-weight path guarantee constraints, green wave connection dynamic compensation constraints, and travel time flexible constraints.

[0057] The generation module is used to solve for the optimal signal timing parameters based on the objective function and the cooperative optimization system, and generate a multi-path coordinated control scheme.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] This invention further optimizes the allocation of green wave signals by constructing a coupling mechanism of traffic flow weight, congestion penalty weight, and symmetry compensation weight. Based on this, it increases the length of the green wave band and improves the continuity of the green wave and the road network's anti-interference capability through high-weight path guarantee constraints, dynamic adaptive constraints, and flexible breakpoint constraints. Attached Figure Description

[0060] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of path information according to an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of the green wave bandwidth of each path in the embodiment of the present invention; wherein, (a) is the green wave bandwidth of high-weight paths 1 and 5; and (b) is the green wave bandwidth of high-weight paths 2 and 6.

[0064] Figure 4 This is a schematic diagram of the simulation evaluation results of an embodiment of the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] This invention constructs a multi-path coordinated control model for urban road networks that considers multi-dimensional dynamic characteristics based on a multi-dimensional weighted coupling mechanism. By dynamically adjusting signal timing parameters, it achieves coordinated optimization of multiple paths. For ease of description, the embodiments of this invention use subscripts "i", "j", and "m" to represent the numbers of uplink, downlink, and mixed paths, and use the subscript "k" to represent the k-th intersection.

[0068] Example 1:

[0069] like Figure 1The diagram shown is a schematic representation of the method flow in this embodiment, and the steps include:

[0070] S1. Obtain traffic flow data, congestion index, and bidirectional green wave bandwidth difference data for each path in the road network at intersections.

[0071] This study selects a portion of the road network in the core area of ​​the sub-center of District B in City A, extending from Road C in the north to Middle Road D in the south, and from Road E in the west to Road F in the east, comprising nine intersections. This area is characterized by dense residential communities and numerous office buildings along its road network, and is adjacent to an export processing zone, making it a crucial origin-destination (OD) point for urban commuting.

[0072] Peak hourly traffic flow was collected at each intersection along the route. The survey period was the morning peak on May 15, 2025: 7:00-8:00. The traffic flow direction at each intersection is shown in Table 1.

[0073] Table 1

[0074] .

[0075] Based on relevant literature and traffic data from field surveys, the case study road network mainly consists of 10 paths, with path information as follows: Figure 2 As shown in Table 2.

[0076] Table 2

[0077] .

[0078] S2. Calculate the multidimensional dynamic weight of each path at the intersection based on traffic flow data, congestion index, and bidirectional green wave bandwidth difference data.

[0079] In this embodiment, the multidimensional dynamic weights include: traffic weight, congestion penalty weight, and symmetry compensation weight, and the calculation formulas are shown below in sequence:

[0080] ,

[0081] ,

[0082] ,

[0083] in, , , These are respectively traffic weight, congestion penalty weight, and symmetry compensation weight; q i Let i be the traffic flow at intersection k on path i; Total flow across all paths and directions; This represents the current congestion index; The congestion threshold; , These represent the green wave bandwidths of uplink path i and downlink path j at intersection k, respectively.

[0084] The traffic weight, congestion penalty weight, and symmetry compensation weight are weighted and merged into a multi-dimensional dynamic weight:

[0085] ,

[0086] in, , , These are the multidimensional weights of the uplink path i, downlink path j, and mixed path m at intersection k; This is the weighting factor.

[0087] S3. Construct an objective function with the goal of maximizing the total weighted green wave bandwidth, and embed multidimensional dynamic weights in the objective function.

[0088] In this embodiment, the constructed objective function is expressed as:

[0089] ,

[0090] in, Let M be the green wave bandwidth of the mixed path m at intersection k; M represents the total number of path sets; and N represents the total number of intersection sets.

[0091] S4. Construct a collaborative optimization system that includes high-weight path guarantee constraints, green wave connection dynamic compensation constraints, and travel time flexible constraints.

[0092] To achieve coordinated control of multi-path green waves, this embodiment divides the constraints into three main parts. Each part forms a collaborative optimization system with the objective function through multi-dimensional weights, as detailed below:

[0093] (1) Guarantee constraints for high-weighted paths:

[0094] Phase difference optimization constraints for high-weighted paths: To prioritize green wave connection optimization for high-weighted paths, improve their green wave continuity, and enable vehicles on high-weighted paths to pass more smoothly between intersections, the constraints are as follows:

[0095] Adjusting the phase difference between adjacent intersections based on path-multidimensional dynamic weighting:

[0096] ,

[0097] ,

[0098] in, , Let k be the phase difference between intersections k and k+1. Intersection , The distance between them; , The optimal driving speeds for up path i and down path j; , Intersection The phase difference adjustment amount for uplink path i and downlink path j;

[0099] Additional green light time based on multidimensional dynamic weight allocation:

[0100] ,

[0101] ,

[0102] in, Let i be the green light time at intersection k for path i; The basic green light ratio for intersection k; The green light weight compensation coefficient for the uphill path i at intersection k; The signal cycle duration at the intersection; , These represent the maximum and minimum values ​​of the signal period duration, respectively.

[0103] High-weight path bandwidth guarantee constraints:

[0104] The path weights are converted into bandwidth guarantees using unit weight bandwidth increments, and directly correlated with the weighted terms of the objective function to ensure the minimum bandwidth requirements of high-weight paths, with the following constraints:

[0105] ,

[0106] in, The minimum bandwidth is the baseline. The minimum weight is the benchmark. This represents the bandwidth increment corresponding to a unit weight.

[0107] (2) Dynamic adaptive constraints:

[0108] Green wave connection dynamic compensation constraints, introducing dynamic compensation terms The dynamic compensation item has been revised (adjusted based on historical data) to make the green wave transition at each intersection more accurate:

[0109] ,

[0110] ,

[0111] Establish the correlation equation between phase difference and dynamic compensation term:

[0112] ,

[0113] ,

[0114] in, , These are the uplink paths at intersections k and k+1, respectively. The total duration of the red light to the left of the green wave band. , These represent the total duration of the red light to the right of the green wave band for downlink path j at intersections k and k+1, respectively. , These represent the duration of the green light before and after the green wave band for the uphill path i at intersections k and k+1, respectively. , These represent the duration of the green light before and after the green wave band for the downlink path j at intersections k and k+1, respectively. The uphill path at intersection k The number of signal cycles; The downlink path at intersection k The number of signal cycles; Uplink path The travel time between intersections k and k+1 Intersection Path Upward queuing release time; The downlink path at intersection k Queue release time The uphill path at intersection k Dynamic compensation items; Intersection The dynamic compensation term for the downlink path j; This represents the green wave bandwidth of uplink path i at intersection k; This represents the green wave bandwidth of downlink path j at intersection k.

[0115] A flexible constraint on travel time is constructed to limit the travel time of the up and down paths between each intersection. The specific expression is as follows:

[0116] ,

[0117] ,

[0118] ,

[0119] ,

[0120] in, and These are the maximum and minimum speeds of the uphill path i, respectively; and These are the maximum and minimum travel speeds for downlink path j, respectively. and These are the slack variables for uplink path i and downlink path j between intersection k and k+1, respectively. It is the reciprocal of the intersection period; , Represents the path-intersection association set; Represents the set of uplink paths; This represents the set of downlink paths.

[0121] (3) Auxiliary constraints:

[0122] Green wave breakpoint constraint: In some complex road networks, a completely continuous green wave may be difficult to achieve. By reasonably setting breakpoints, it is possible to better adapt to changes in road conditions and traffic flow, optimize the distribution of the green wave, reduce vehicle delays and stops, and improve the overall operational efficiency of the road network. Specific constraints are as follows:

[0123] ,

[0124] ,

[0125] In the formula, For set Size; For set Size; and These represent the allowed number of breakpoints for the uplink and downlink paths, respectively.

[0126] S4. Solve for the optimal signal timing parameters based on the objective function and the collaborative optimization system to generate a multi-path coordinated control scheme.

[0127] The mixed-integer linear programming model containing the objective function and the co-optimization system is solved using a mathematical programming solver, and the signal period, phase difference and green ratio parameters of each intersection are output.

[0128] Example 2:

[0129] To verify the effectiveness of the model of this invention in multi-path green wave coordinated control, this embodiment uses Python combined with the GUROBI solver to solve the model based on the survey and data of 9 intersections and 10 paths in the road network.

[0130] Since the original road network did not implement green wave coordination control, the traffic signal optimization software SYNCHRO was used to optimize the network of all intersections. The optimized timing scheme is shown in Table 3.

[0131] Table 3

[0132] .

[0133] During the solution process, traffic flow weights are calculated based on peak traffic flow data at intersections along the road network, and congestion penalty weights are generated using historical congestion data. A symmetry compensation weight is calculated considering the difference in bidirectional green wave bandwidth. Based on the path traffic flow data and weight calculation formulas in Table 2, and referring to relevant weight thresholds, paths 1, 2, 5, and 6 are classified as high-weight paths, while paths 3, 4, 7, 8, 9, and 10 are classified as low-weight paths. The number of breakpoints is set to q=1 for high-weight paths and q=2 for low-weight paths.

[0134] The optimized model and SYNCHRO timing in this embodiment, as well as the green wave bandwidth of each path in the Yang model scheme, are shown in Table 4. Figure 3 As shown.

[0135] Table 4

[0136] .

[0137] From Table 4 and Figure 3 As can be seen, compared to the SYNCHRO and Yang models, the improved green wave bandwidth of this model is primarily reflected in the four high-weighted paths. For example, the green wave bandwidth of path 1 reaches 22 seconds, an improvement of 37.5% compared to the SYNCHRO scheme and 22.2% compared to the Yang model. This is because this model, through the coupled calculation of traffic weight and congestion penalty weight, performs phase difference compensation during the morning peak congestion period, thereby increasing the corresponding phase green light time and thus increasing the green wave bandwidth. The green wave bandwidth of path 7 increased from 12 seconds in the Yang model to 17 seconds, verifying the optimization effect of symmetric compensation weight on secondary arterial roads.

[0138] In this embodiment, 1-2 breakpoints are actively allowed in paths 4 and 9. Although this results in path 4 being 9 seconds shorter than the SYNCHRO model, it avoids the vehicle queue overflow caused by the forced continuous green wave, thus improving the overall green wave bandwidth utilization of the road network by 15.3%.

[0139] The comparison shows that the total green wave bandwidth of the model in this embodiment is better than that of the SYNCHRO and Yang models. Compared with the SYNCHRO and Yang models, the bandwidth of the model in this embodiment is optimized by 28.57% and 13.21%, respectively, and the high-weight paths are improved by 51.61% and 46.88% compared with the SYNCHRO and Yang models, respectively.

[0140] To verify the control effect of the model on green waves of multiple paths in the road network, simulation experiments were conducted using SYNCHRO to solve the evaluation indexes corresponding to the multi-path control schemes of the road network optimized by the above three models, as shown in Table 5.

[0141] Table 5

[0142] .

[0143] As can be seen from Table 5, this embodiment shows the most significant improvement in average vehicle delay for each path in the road network, particularly for high-weight paths. The average vehicle delay for path 1 is reduced from 63 seconds in SYNCHRO to 54 seconds, and is 14% lower than that in the Yang model.

[0144] Regarding the average number of stops per vehicle on each path in the road network, the model in this embodiment shows good optimization results for the number of stops on congested road sections such as paths 3 and 6. Path 6, due to its passage through a commercial area, is relatively congested. This model dynamically adjusts the signal timing through congestion penalty weights, reducing the average number of stops per vehicle from 2.73 by 1.35 compared to SYNCHRO's model, and by 0.7 compared to the Yang model. The number of stops on path 4 increases compared to the Yang model. This is because the model actively allows one breakpoint on this road section, temporarily interrupting the green wave to alleviate sudden congestion.

[0145] In addition to the aforementioned paths, for the overall traffic of all vehicles in the road network, the simulation evaluation results of the timing schemes optimized by the three models are shown below. Figure 4 .

[0146] Example 3:

[0147] This embodiment also provides a multi-path coordinated control system for urban road networks considering multi-dimensional dynamic characteristics, including: a data acquisition module, a calculation module, an embedding module, a construction module, and a generation module; the data acquisition module is used to acquire traffic flow data, congestion index, and bidirectional green wave bandwidth difference data of each path at intersections in the road network; the calculation module is used to calculate the multi-dimensional dynamic weights of each path at intersections based on the traffic flow data, congestion index, and bidirectional green wave bandwidth difference data; the embedding module is used to construct an objective function with the goal of maximizing the total weighted green wave bandwidth, embedding multi-dimensional dynamic weights in the objective function; the construction module is used to construct a collaborative optimization system including high-weight path guarantee constraints, green wave connection dynamic compensation constraints, and travel time flexibility constraints; the generation module is used to solve for the optimal signal timing parameters based on the objective function and the collaborative optimization system, and generate a multi-path coordinated control scheme.

[0148] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for multi-path coordinated control of urban road network considering multi-dimensional dynamic characteristics, characterized in that, The application relates to a method for multi-path coordinated control of a road network. Traffic flow data, congestion index and bidirectional green wave bandwidth difference data of each path in the road network are acquired; Multi-dimensional dynamic weights of each path at intersections are calculated according to the traffic flow data, congestion index and bidirectional green wave bandwidth difference data; A target function is constructed with the aim of maximizing the total weighted green wave bandwidth, and the multi-dimensional dynamic weights are embedded in the target function; The multi-dimensional dynamic weights include flow weight, congestion penalty weight and symmetry compensation weight, and the calculation formulas of the flow weight, congestion penalty weight and symmetry compensation weight are as follows: ， ， ， wherein, are the flow weight, the congestion penalty weight, and the symmetry compensation weight, respectively; qi is the traffic flow on intersection k on path i; is the total flow for all paths and directions; is the current congestion index; is the congestion threshold; 、 denote the uplink paths i , the downlink paths j , and the green wave bandwidth at the intersection k , respectively. The flow weight, congestion penalty weight and symmetry compensation weight are fused into the multi-dimensional dynamic weights: ， wherein, , , are an uplink path i , a downlink path j , a hybrid path m at an intersection k ; is a weighting factor; A coordinated optimization system is constructed, which includes high-weight path guarantee constraints, dynamic adaptability constraints and auxiliary constraints; The high-weight path guarantee constraints are established by adjusting the phase difference of adjacent intersections based on the multi-dimensional dynamic weights of the paths: ， ， wherein, , is the phase difference of the intersection k , k +1; is the distance between the intersection k , k +1; , is the optimal driving speed of the uplink path i , the downlink path j ; , is the phase difference adjustment amount of the uplink path k , the downlink path i at the intersection j ; , are respectively the green wave bandwidths of the uplink path i , the downlink path j , at the intersection k ; Additional green light time is allocated based on the multi-dimensional dynamic weights: ， ， Wherein, is the path i At the intersection k Green light time; is the base green ratio of the intersection k ; is the green light weight compensation coefficient of the uplink path k at the intersection i ; is the signal cycle length of the intersection; , The maximum and minimum values of the signal cycle length, respectively; High-weight path bandwidth guarantee constraints: The path weight is converted into bandwidth guarantee amount through unit weight bandwidth increment, and is directly associated with the weighted term of the target function, and the constraints are as follows: ， wherein, is a reference minimum bandwidth; is a reference minimum weight; is a bandwidth increment corresponding to a unit weight. The optimal signal timing parameters are solved based on the target function and the coordinated optimization system, and a multi-path coordinated control scheme is generated.

2. The method of claim 1, wherein, The dynamic adaptability constraints are established by establishing a correlation equation between the phase difference and a dynamic compensation term: Introducing a dynamic compensation term Amending the green wave connection condition: ， ， The dynamic adaptability constraints are also established by constructing a driving time flexibility constraint: ， ， in, , Intersections k , k +1 uplink path i The total duration of the red light to the left of the green wave band. , Intersections k , k Downlink path at +1 j The total duration of the red light to the right of the green wave band. , Intersections k , k +1 uplink path i The duration of the green light before and after the green wave band. , Intersections k , k Downlink path at +1 j The duration of the green light before and after the green wave band. Intersection k Upward path i The number of signal cycles; Intersection k Downward path j The number of signal cycles; Uplink path i At the intersection k and k Travel time between +1 Intersection k +1 path i Upward queuing release time; Intersection k Out of the downlink path j Queue release time Intersection k Upward path i Dynamic compensation items; Intersection k Downward path j Dynamic compensation items; Indicates the uplink path i At the intersection k Green wave bandwidth; Indicates the downlink path j At the intersection k The green wave bandwidth.

3. The method of claim 2, wherein, The auxiliary constraints are constructed as follows: The optimal signal timing parameters are solved by using a mathematical programming solver to solve a mixed integer linear programming model containing the target function and the coordinated optimization system, and outputting the signal cycle, phase difference and green ratio parameters of each intersection. ， ， ， ， wherein, and are the maximum and minimum travel speeds of the uplink path i, respectively; and are the maximum and minimum travel speeds of the downlink path j, respectively; and are the relaxation variables of the uplink path i and the downlink path j between the intersections k and k+1, respectively; Z is the inverse of the intersection cycle; , denotes the path-intersection association set; denotes the uplink path set; denotes the downlink path set.

4. The method of claim 3, wherein, The application further relates to a multi-path coordinated control system for a road network. ， ， wherein is the size of the set ; is the size of the set ; and are the number of allowed break points for the uplink and downlink, respectively.

5. The method of claim 1, wherein, The system comprises a collection module, a calculation module, an embedding module, a construction module and a generation module.

6. A multi-path coordinated control system for urban road networks considering multi-dimensional dynamic features, the system being configured to implement the method of any one of claims 1-5, characterized in that, ​ ​

Images