Urban road multi-path coordination control method and system based on improved AM-Band model

Abstract

The invention discloses an urban road multi-path coordination control method and system based on an improved AM-Band model, and the method comprises the following steps: carrying out the improvement of an original AM-Band model based on path flow and steering demands, and obtaining a multi-path-oriented green wave bandwidth maximum model; based on an indicator light signal device between an upstream intersection and a downstream intersection, determining a speed guidance range, and establishing a delay minimization model based on optimal speed guidance; and solving the green wave bandwidth maximum model and the delay minimization model by using a locust optimization algorithm based on multiple targets to obtain a signal timing scheme. Aiming at the problems of insufficient green wave bandwidth and path competition in continuous intersections, comprehensive optimization of urban traffic is realized through a two-stage optimization model in combination with signal timing and vehicle speed guidance.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban transportation, and particularly relates to a multi-path coordinated control method and system for urban roads based on an improved AM-Band model. Background Art

[0002] With the acceleration of the urbanization process, the complexity of urban road networks is increasing day by day. Especially during the peak commuting hours on weekdays, residents in multiple regions flow towards the city center or other work destinations, forming complex paths with multiple commuting OD (origin-destination) paths. The competitiveness and interweaving of such paths not only increase the difficulty of traffic management but also pose new challenges to traditional traffic signal coordinated control strategies. Against this background, the research on urban traffic signal coordinated control has gradually shifted from the green wave coordination of single arterial roads to the more complex coordinated optimization of multiple commuting paths.

[0003] Traditional green wave coordinated control began with the MAXBAND model proposed by Little. It optimized signal cycle, phase difference, and driving speed through linear programming methods, laying the foundation for subsequent research. However, the MAXBAND model did not fully consider the differences in different traffic flow directions. To solve this problem, Gartner introduced bandwidth variation in the MULTIBAND model to provide a more flexible green wave scheme for different traffic flow directions. Based on this, Zhang proposed the MULTIBAND model by relaxing the restriction of symmetric bandwidth to better perform green wave coordinated control for different traffic flows and road conditions.

[0004] However, existing research mostly focuses on speed guidance on arterial roads or under specific conditions. For complex arterial networks with multi-path integration, how to further optimize the speed guidance strategy and make it more closely combined with green wave coordinated control is a problem worthy of in-depth discussion. Summary of the Invention

[0005] The present invention aims to solve the deficiencies of the prior art and provides the following solutions:

[0006] A multi-path coordinated control method for urban roads based on an improved AM-Band model, comprising the following steps:

[0007] Improve the original AM-Band model based on path flow and turning demand to obtain a green wave bandwidth maximum model for multi-paths;

[0008] Determine the speed guidance range based on the indicator signal devices between upstream and downstream intersections, and establish a delay minimization model based on optimal speed guidance;

[0009] Using the multi-objective based locust optimization algorithm, solve the maximum green wave bandwidth model and the delay minimization model to obtain the signal timing plan.

[0010] Preferably, the method for constructing the maximum green wave bandwidth model includes:

[0011] Improve the original AM-Band model to obtain the multi-path green wave objective function:

[0012]

[0013] Among them, Z represents the weighted sum of the green wave bandwidths of the upstream and downstream critical paths of all intersections on the entire road section of the optimization objective, represents the weight of path i in the upstream direction at intersection j, represents the green wave bandwidth of the left half of path i in the upstream direction at intersection j, represents the green wave bandwidth of the right half of path i in the upstream direction at intersection j, represents the weight of path i in the downstream direction at intersection j, represents the green wave bandwidth of the left half of path i in the downstream direction at intersection j, represents the green wave bandwidth of the right half of path i in the downstream direction at intersection j, M represents the total number of intersections, N j represents the number of critical paths at intersection j;

[0014] Apply several constraint conditions to the multi-path green wave objective function to obtain the maximum green wave bandwidth model.

[0015] Preferably, the method for constructing the delay minimization model includes:

[0016] Determine the speed guidance range:

[0017]

[0018] Among them, L c represents the speed guidance area control range, V max represents the maximum speed of the road section, V min represents the minimum speed of the road section, a sp represents the acceleration at which the driver feels comfortable, a sd represents the deceleration at which the driver feels comfortable, t rt represents the reaction time of the driver;

[0019] When the vehicle cannot pass through the intersection at its original speed in the current signal cycle and can pass through the intersection by acceleration guidance, generate a green light guidance model based on the acceleration guidance strategy:

[0020]

[0021] Among them, V t (j, k) represents the theoretical optimal speed of vehicle k in the coordinated phase of intersection j, L d (j, k) represents the distance of vehicle k in the coordinated phase of intersection j from the intersection, L s (j, k) is the length of the queue of vehicle k in the coordinated phase of intersection j, V a (j, k) represents the average driving speed of vehicle k in the coordinated phase of intersection j;

[0022] When the vehicle cannot pass through by accelerating in the current signal cycle but can pass through by decelerating in the next cycle, so as to achieve passing without stopping, a green - light guiding model is generated based on the deceleration guiding strategy:

[0023]

[0024] Among them, t(j, x + 1) represents the green - light start time of the coordinated phase of intersection j in the (x + 1)-th cycle, t n represents the current time;

[0025] When the vehicle reaches the end of the queue and the start - up fluctuation reaches the end of the queue, but the vehicle cannot pass through the intersection, a red - light guiding model is generated based on the deceleration guiding strategy:

[0026]

[0027] Among them, represents the speed at which the saturated traffic flow of intersection j passes through the intersection;

[0028] Based on the above - mentioned speed guiding range, the green - light guiding model and the red - light guiding model, the delay - minimization model is constructed:

[0029]

[0030] Among them, D(j) represents the total delay at intersection j on the path, L(j) represents the delay detection area of intersection j, represents the average driving speed of the remaining sections except the guiding area in the delay detection area of intersection j, U(j) represents the number of passing vehicles, v f represents the free - flow speed.

[0031] Preferably, the method for solving the green - wave bandwidth maximum model and the delay - minimization model by using the locust optimization algorithm includes:

[0032] Randomly generate a population composed of m locust individuals, each locust represents a potential traffic signal adjustment scheme, and initialize the algorithm parameters at the same time;

[0033] Check the constraint conditions for each locust individual in the group. If it is determined that the signal scheme of a locust individual does not conform to traffic rules or safety requirements, remove the non-compliant individuals from the group;

[0034] For each locust individual, calculate the fitness based on the maximum green wave bandwidth model and the delay minimization model, identify the non-dominated solution set according to the Pareto dominance principle, and evaluate and sort the non-dominated solution set;

[0035] Select the locust individual with the best distribution density in the non-dominated solution set as the global optimal solution Gbest for this iteration, and determine the current position of this individual as the historical optimal position of the individual;

[0036] According to the global optimal solution Gbest and the historical optimal positions of each locust individual, adjust the weight coefficients of the algorithm, and update the positions and speeds of each locust individual;

[0037] If the position of the current locust individual is better than the historical optimal position, replace the historical optimal position with the current position, select the global optimal solution according to the optimal density distance, and update the global optimal solution Gbest. If the fitness of the global optimal solution Gbest is higher than the previous optimal fitness at this time, add this Gbest to the new non-dominated solution set and remove the dominated individuals from the solution set;

[0038] Detect whether the preset maximum number of iterations is reached, or whether the accuracy of the locust individuals meets the set requirements. If either condition is satisfied, output the maximum dominance Pareto solution of the corresponding fitness value and the corresponding signal timing scheme.

[0039] The present invention also provides an urban road multi-path coordination control system based on an improved AM-Band model. The system applies the method described in any one of the above, and includes: a first model construction module, a second model construction module, and a scheme solving module;

[0040] The first model construction module improves the original AM-Band model based on path flow and turning requirements to obtain a maximum green wave bandwidth model for multi-paths;

[0041] The second model construction module determines the speed guidance range based on the indicator signal devices between upstream and downstream intersections, and establishes a delay minimization model based on the best speed guidance;

[0042] The scheme solving module uses a multi-objective locust optimization algorithm to solve the maximum green wave bandwidth model and the delay minimization model to obtain a signal timing scheme.

[0043] Preferably, the working process of the first model construction module includes:

[0044] Improve the original AM - Band model to obtain a multi - path green wave objective function:

[0045]

[0046] Among them, Z represents the weighted sum of the green wave bandwidths of the upstream and downstream critical paths of all intersections on the entire section of the optimization target. represents the weight of path i in the upstream direction at intersection j. represents the left - hand half green wave bandwidth of path i in the upstream direction at intersection j. represents the right - hand half green wave bandwidth of path i in the upstream direction at intersection j. represents the weight of path i in the downstream direction at intersection j. represents the left - hand half green wave bandwidth of path i in the downstream direction at intersection j. represents the right - hand half green wave bandwidth of path i in the downstream direction at intersection j. M represents the total number of intersections, and N j represents the number of critical paths at intersection j;

[0047] Apply a number of constraint conditions to the multi - path green wave objective function to obtain the maximum green wave bandwidth model.

[0048] Preferably, the working process of the second model construction module includes:

[0049] Determine the speed guidance range:

[0050]

[0051] Among them, L c represents the speed guidance area control range, V max represents the maximum speed of the section, V min represents the minimum speed of the section, a sp represents the acceleration at which the driver feels comfortable, a sd represents the deceleration at which the driver feels comfortable, t rt represents the reaction time of the driver;

[0052] When the vehicle cannot pass through the intersection at the original speed in the current signal cycle and can pass through the intersection through acceleration guidance, generate a green light guidance model based on the acceleration guidance strategy:

[0053]

[0054] Among them, V t (j,k) represents the theoretical optimal speed of vehicle k in the coordinated phase of intersection j, L d(j, k) represents the distance of vehicle k in the coordinated phase of intersection j from the intersection, L s (j, k) is the length of the queue of vehicle k in the coordinated phase of intersection j, V a (j, k) represents the average driving speed of the vehicle in the coordinated phase of intersection j;

[0055] When the vehicle cannot pass through by accelerating in the current signal cycle but can pass through by decelerating in the next cycle, so as to achieve passing without stopping, a green light guidance model is generated based on the deceleration guidance strategy:

[0056]

[0057] Among them, t(j, x + 1) represents the green light start time of the coordinated phase of intersection j in the (x + 1)-th cycle, t n represents the current time;

[0058] When the vehicle travels to the end of the queue and the start-up fluctuation is transmitted to the end of the queue, but the vehicle cannot pass through the intersection, a red light guidance model is generated based on the deceleration guidance strategy:

[0059]

[0060] Among them, represents the speed at which the saturated traffic flow of intersection j passes through the intersection;

[0061] Based on the speed guidance range, the green light guidance model and the red light guidance model, the delay minimization model is constructed:

[0062]

[0063] Among them, D(j) represents the total delay at intersection j on the path, L(j) represents the delay detection area of intersection j, represents the average driving speed of the remaining sections except the guidance area in the delay detection area of intersection j, U(j) represents the number of passing vehicles, v f represents the free flow speed.

[0064] Preferably, the working process of the solution module includes:

[0065] Randomly generate a population composed of m locust individuals, each locust represents a potential traffic signal adjustment plan, and initialize the algorithm parameters at the same time;

[0066] Check the constraint conditions for each locust individual in the population. If it is judged that the signal plan of a locust individual does not meet the traffic rules or safety requirements, the non-compliant individual will be removed from the population;

[0067] For each locust individual, calculate the fitness based on the maximum green wave bandwidth model and the delay minimization model, identify the non-dominated solution set according to the Pareto domination principle, and evaluate and rank the non-dominated solution set;

[0068] Select the locust individual with the best distribution density in the non-dominated solution set as the global optimal solution Gbest for this iteration, and determine the current position of this individual as the historical optimal position of the individual;

[0069] Adjust the weight coefficients of the algorithm according to the global optimal solution Gbest and the historical optimal positions of each locust individual, and update the positions and speeds of each locust individual;

[0070] If the position of the current locust individual is better than the historical optimal position, replace the historical optimal position with the current position, select the global optimal solution according to the optimal density distance, and update the global optimal solution Gbest. If the fitness of the global optimal solution Gbest is higher than the previous optimal fitness at this time, add this Gbest to the new non-dominated solution set and remove the dominated individuals in the solution set;

[0071] Detect whether the preset maximum number of iterations is reached, or whether the accuracy of the locust individuals meets the set requirements. If either condition is satisfied, output the maximum domination degree Pareto solution corresponding to the fitness value and its corresponding signal timing plan.

[0072] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0073] The present invention significantly reduces the average vehicle delay on the high-traffic path, with a maximum reduction of approximately 12.5%, and the average vehicle stop times also decrease by approximately 0.21 times, indicating that the proposed solution has particularly obvious optimization effects on high-traffic sections. Overall, the present invention performs excellently in terms of improving the green wave bandwidth, reducing the average vehicle delay and the average vehicle stop times, especially the signal timing adjustment effect on high-traffic sections is particularly prominent. Aiming at the problems of insufficient green wave bandwidth and path competition in continuous intersections, through a two-stage optimization model, combined with signal timing and vehicle speed guidance, the comprehensive optimization of urban traffic is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the present invention, the following briefly introduces the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0075] Figure 1 It is a schematic flowchart of the method according to the embodiment of the present invention;

[0076] Figure 2 The coordinated intersection multi-path of the embodiment of the present invention. Specific embodiments

[0077] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0078] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0079] Embodiment 1

[0080] In path coordination control, there is a close relationship between the green wave maximum bandwidth model and the delay model. The green wave maximum bandwidth model mainly focuses on improving the traffic efficiency of vehicles by optimizing traffic signal timing, achieving higher passing rates and smoother traffic flow. Relatively speaking, the delay model focuses on the time loss caused by traffic signal control, quantifying the waiting time of vehicles before red lights and the stopping time caused by traffic signal changes. The two complement each other. The green wave maximum bandwidth model can provide a basis for optimizing the delay model. By evaluating the delay situations of different signal timing schemes, the traffic signal settings can be further improved, enabling the overall traffic system to achieve efficient passage and effectively reduce delays. Therefore, by combining the advantages of these two models, the efficiency and operation quality of the traffic system can be comprehensively improved.

[0081] In this embodiment, as Figure 1 shown, a multi-path coordinated control method for urban roads based on an improved AM-Band model includes the following steps:

[0082] S1. Improve the original AM-Band model based on path flow and turning demand to obtain a green wave bandwidth maximum model for multi-paths.

[0083] The method for constructing the green wave bandwidth maximum model includes: improving the original AM-Band model to obtain a multi-path green wave objective function:

[0084]

[0085] where Z represents the sum of the weighted green wave bandwidths of the upstream and downstream critical paths of all intersections on the entire road section of the optimization objective, represents the weight of the upstream direction of path i at intersection j, Denote the left - hand part of the green - wave bandwidth of path \(i\) at intersection \(j\) in the upstream direction. Denote the right - hand part of the green - wave bandwidth of path \(i\) at intersection \(j\) in the upstream direction. Denote the weight of path \(i\) at intersection \(j\) in the downstream direction. Denote the left - hand part of the green - wave bandwidth of path \(i\) at intersection \(j\) in the downstream direction. Denote the right - hand part of the green - wave bandwidth of path \(i\) at intersection \(j\) in the downstream direction. \(M\) represents the total number of intersections, \(N\) j Denote the number of critical paths at intersection \(j\).

[0086] Apply several constraint conditions to the multi - path green - wave objective function to obtain the maximum green - wave bandwidth model.

[0087] In this embodiment, ensure that the paths with larger traffic volumes can obtain wider green - wave bands, thereby improving the efficiency and smoothness of the entire traffic network. By constructing the following constraint conditions, traffic congestion can be reduced and the road capacity can be improved:

[0088]

[0089] where \(k\) j Denote the target ratio of the upstream and downstream bandwidths at intersection \(j\).

[0090] The upper and lower limits of each intersection cycle are as follows:

[0091]

[0092] where \(C1\) represents the lower limit of the cycle and \(C2\) represents the upper limit of the cycle.

[0093] In order to ensure that on each path \(i\) at each intersection \(k\), the green - wave bandwidths in the upstream and downstream directions do not overlap with the red - light signal time, construct the following constraint conditions to ensure that vehicles can pass smoothly within the green - wave bandwidth and avoid stops and delays caused by red - light signals:

[0094]

[0095]

[0096] where \(w\) i,j Denote the part of the green - light duration before the green - wave band of the upstream path \(i\) at intersection \(j\), \(w\) i,j+1 Denote the part of the green - light duration after the green - wave band of the upstream path \(i\) at intersection \(j + 1\). Denote the part of the green - light duration after the green - wave band of the downstream path \(i\) at intersection \(j\). Denote the part of the green - light duration before the green - wave band of the downstream path \(i\) at intersection \(j + 1\), \(r\) i,jDenote the total red light duration on the left side of the green wave band of path \(i\) at intersection \(j\), \(r\) i,j+1 Denote the total red light duration on the left side of the green wave band of path \(i\) at intersection \(j + 1\), Denote the total red light duration on the left side of the green wave band of path \(i\) at intersection \(j\), Denote the total red light duration on the left side of the green wave band of path \(i\) at intersection \(j + 1\), \(\tau\) i,j+1 Denote the upstream queue release time of path \(i\) at intersection \(j + 1\), Denote the downstream queue release time of path \(i\) at intersection \(j\).

[0097] To ensure the effective allocation of the green wave bandwidth and avoid allocating resources to paths that do not actually obtain the green wave bandwidth. The following constraint is proposed to help the model select paths during the decision-making process, ensuring that only those paths that truly need and should obtain the green wave bandwidth will be considered in the optimization of the signal timing:

[0098]

[0099] Wherein, Is a binary decision variable, indicating whether there is a green wave bandwidth, \(z\) represents the cycle derivative, \(b\) e Represents the effective green light time, \(L\) represents a very large constant.

[0100] To ensure that vehicle flows can continuously pass through on the inbound and outbound paths at intersections and form an effective green wave band, the following cyclic constraint formula is constructed:

[0101]

[0102] Wherein, \(\theta\) j Represents the phase difference of intersection \(j\), \(n\) i,j Represents the number of signal cycles of the upstream path \(i\) at intersection \(j\), Represents the number of signal cycles of the downstream path \(i\) at intersection \(j\), \(t\) j Represents the driving time of the upstream path \(i\) from intersection \(j\) to \(j + 1\), Represents the driving time of the downstream path \(i\) from intersection \(j\) to \(j + 1\); These constraints consider the waiting time of vehicles at intersections, the green wave bandwidth, and the time synchronization between adjacent intersections. For outbound paths that do not participate in the green wave progression, the constraint conditions will be adjusted to avoid allocating invalid green wave bandwidths to these paths, thus ensuring the feasibility of the solution of the entire optimization model.

[0103] Set the upper and lower limits of the driving speed for each section. Such speed control helps to improve road safety, reduce accident risks, and maintain the smoothness and efficiency of traffic flow. The constraint conditions are as follows:

[0104]

[0105] Among them, f j represents the upper speed limit for driving at intersection j, represents the upper downstream driving speed limit at intersection j, e j represents the lower speed limit for driving at intersection j, represents the lower downstream driving speed limit at intersection j, d j represents the distance of the upstream path from intersection j to j + 1, represents the distance of the downstream path from intersection j to j + 1.

[0106] Setting the upper and lower limits for the speed change between consecutive road segments. This means that when a vehicle transitions from one road segment to the next, its speed change (acceleration or deceleration) must be kept within a certain range. This helps to avoid traffic safety problems caused by sudden acceleration or deceleration of vehicles, and also helps to reduce fuel consumption and emissions. The constraint conditions are as follows:

[0107]

[0108] Among them, h j represents the lower limit of the speed change at intersection j, g j represents the upper limit of the speed change at intersection j, represents the lower limit of the speed change of the downstream path at intersection j, represents the upper limit of the speed change of the downstream path at intersection j.

[0109] In order to ensure that when optimizing traffic signals, the following constraint equations are constructed to ensure that all relevant time and bandwidth parameters are reasonable and in line with the actual situation:

[0110]

[0111] S2. Based on the indicator signal devices between upstream and downstream intersections, determine the speed guidance range and establish a delay minimization model based on the optimal speed guidance.

[0112] The method for constructing the delay minimization model includes:

[0113] Determine the shortest control range of the vehicle speed guidance area, taking into account that at any vehicle speed, the vehicle has sufficient time to adjust its speed. When determining the longest control range, it is necessary to ensure that the vehicle can pass through the intersection within one signal cycle while considering the driver's reaction time. Therefore, determine the speed guidance range:

[0114]

[0115] Among them, L c represents the control range of the speed guidance area, Vmax Represents the maximum speed of a road section, V min Represents the minimum speed of a road section, a sp Represents the acceleration at which the driver feels comfortable, a sd Represents the deceleration at which the driver feels comfortable, t rt Represents the reaction time of the driver;

[0116] When the vehicle cannot pass through the intersection at its original speed during the current signal cycle and can pass through the intersection by accelerating, a green - light guiding model is generated based on the acceleration - guiding strategy:

[0117]

[0118] Among them, V t (j, k) represents the theoretical optimal speed of vehicle k in the coordinated phase of intersection j, L d (j, k) represents the distance of vehicle k in the coordinated phase of intersection j from the intersection, L s (j, k) the length of vehicle k queuing in the coordinated phase of intersection j, V a (j, k) represents the average driving speed of vehicle k in the coordinated phase of intersection j;

[0119] When the vehicle cannot pass through by accelerating during the current signal cycle but can pass through by decelerating in the next cycle to achieve passing without stopping, a green - light guiding model is generated based on the deceleration - guiding strategy:

[0120]

[0121] Among them, t(j, x + 1) represents the green - light starting time of the coordinated phase of intersection j in the (x + 1) - th cycle, t n Represents the current moment;

[0122] When the vehicle reaches the end of the queue and the starting wave propagates to the end of the queue, but the vehicle cannot pass through the intersection, a red - light guiding model is generated based on the deceleration - guiding strategy:

[0123]

[0124] Among them, Represents the speed at which the saturated traffic flow of intersection j passes through the intersection;

[0125] In vehicle speed guidance, the vehicle travel delay refers to the difference between the actual travel time of the vehicle on a specific road section and the time it would take to travel the same road section at the free - flow vehicle speed. In this embodiment, the delay is defined as the time difference between passing through the delay detection area (with a length greater than the guiding area) within a certain time, the target vehicle speed and the time required to pass through the intersection at the free - flowing vehicle speed; therefore, a delay - minimization model is constructed based on the speed - guidance range, green - light guiding model, and red - light guiding model:

[0126]

[0127] Among them, D(j) represents the total delay at intersection j on the path, and L(j) represents the delay detection area of intersection j. represents the average driving speed of the remaining sections in the delay detection area of intersection j except the guiding area, U(j) represents the number of passing vehicles, and v f represents the free flow speed.

[0128] S3. Use the multi-objective locust optimization algorithm to solve the green wave bandwidth maximization model and the delay minimization model to obtain the signal timing plan.

[0129] Since the decision variables are multi-dimensional, with the increase in the number of coordinated intersections and paths, the dimension increases accordingly, making traditional analytical methods, graphical methods, etc. become complex and heavy in the calculation process, with poor efficiency and unable to achieve the optimization purpose. The multi-objective locust optimization algorithm (MOGOA) is a multi-objective intelligent optimization algorithm for solving problems. It has the advantages of high efficient global search ability, easy implementation, good solution quality, etc. This algorithm draws on the foraging habits of locusts, where the position of each search individual (i.e., locust) is a possible problem solution, and its mathematical model is shown as follows:

[0130] X l = S l + G l + A l

[0131] Among them, X l represents the position of the l-th locust in the population, S l represents being affected by the social force, G l represents being affected by gravity, and A l represents being affected by wind force.

[0132] The social interaction between populations is the most important factor affecting the movement of locusts, which can be described by the following formula:

[0133]

[0134]

[0135] Among them, d ml represents the distance between the m-th and the l-th locusts, represents the unit vector between the m-th and the l-th locusts, and s(r) represents the social force between locusts.

[0136] The influences of locust gravity and wind force can be respectively expressed by the following formulas:

[0137]

[0138] Among them, g represents the gravitational constant, represents the unit vector of gravitational force, t represents the drift constant in the wind direction, represents the unit vector in the wind direction.

[0139] The update model of the locust position can be expressed by the following formula:

[0140]

[0141] Among them, ub d represents the upper bound of the position in the d dimension, lb d represents the lower bound of the position in the d dimension, represents the position where the leading individual with advantages in the current population is located, and c represents the linear reduction coefficient.

[0142] The multi-objective locust algorithm finds the Pareto optimal solution set by evaluating and updating the positions of individuals in each generation. This solution set represents solutions that achieve a balance among all objectives. The algorithm uses an external storage archive to record the optimal individuals (solutions) found during the optimization process and maintains and updates this archive set through the domination relationship, that is, by judging whether one solution is not worse than another solution in all objectives.

[0143] In this embodiment, the method for solving the maximum green wave bandwidth model and the minimum delay model using the locust optimization algorithm includes:

[0144] Randomly generate a population consisting of m locust individuals. Each locust represents a potential traffic signal adjustment scheme, including multi-dimensional parameter vectors such as the traffic signal cycle, the green light time of each phase at each intersection, and the phase difference, etc.; at the same time, initialize the algorithm parameters, such as the maximum speed of the locust individual, the learning factor, and the inertia weight, etc.;

[0145] Check the constraint conditions for each locust individual in the population. If it is judged that the signal scheme of a locust individual does not conform to traffic rules or safety requirements, the non-compliant individual is removed from the population;

[0146] Starting with the iteration counter t set to 1, for each locust individual, calculate the fitness based on the maximum green wave bandwidth model and the minimum delay model, and identify the non-dominated solution set according to the Pareto domination principle, and evaluate and sort the non-dominated solution set;

[0147] Select the locust individual with the best distribution density in the non-dominated solution set as the global optimal solution Gbest for this iteration, and determine the current position of this individual as the historical optimal position of the individual;

[0148] Increment the iteration counter by one (i.e., n = n + 1). In the new iteration process, adjust the weight coefficients of the algorithm according to the global optimal solution Gbest and the historical optimal positions p of each locust individual, and update the positions and velocities of each locust individual.

[0149] If the position of the current locust individual is better than the historical optimal position p, replace the historical optimal position with the current position, select the global optimal solution according to the optimal density distance, and update the global optimal solution Gbest. If the fitness of the global optimal solution Gbest is higher than the previous optimal fitness at this time, add this Gbest to the new non-dominated solution set and remove the dominated individuals in the solution set.

[0150] Detect whether the preset maximum number of iterations is reached, or whether the accuracy of the locust individuals meets the set requirements. If either condition is satisfied, output the maximum domination degree Pareto solution of the corresponding fitness value and the corresponding signal timing plan.

[0151] Embodiment 2

[0152] In this embodiment, as Figure 1 shown, an urban road multi-path coordination control system based on an improved AM-Band model includes: a first model construction module, a second model construction module, and a solution solving module;

[0153] The first model construction module improves the original AM-Band model based on path flow and turning demand to obtain a green wave bandwidth maximum model for multiple paths.

[0154] The working process of the first model construction module includes:

[0155] Improve the original AM-Band model to obtain a multi-path green wave objective function:

[0156]

[0157] Among them, Z represents the total weighted green wave bandwidth of the upstream and downstream critical paths of all intersections on the entire road section of the optimization target, represents the weight of path i in the upstream direction at intersection j, represents the left half of the green wave bandwidth of path i in the upstream direction at intersection j, represents the right half of the green wave bandwidth of path i in the upstream direction at intersection j, represents the weight of path i in the downstream direction at intersection j, represents the left half of the green wave bandwidth of path i in the downstream direction at intersection j, represents the right half of the green wave bandwidth of path i in the downstream direction at intersection j, M represents the total number of intersections, N jDenote the number of critical paths at intersection j; impose several constraint conditions on the multi-path green wave objective function to obtain the maximum green wave bandwidth model.

[0158] The second model construction module determines the speed guidance range based on the indicator signal devices between the upstream and downstream intersections, and establishes a delay minimization model based on the optimal speed guidance.

[0159] The workflow of the second model construction module includes: determining the speed guidance range:

[0160]

[0161] Among them, L c Denote the speed guidance area control range, V max Denote the maximum speed of the road section, V min Denote the minimum speed of the road section, a sp Denote the acceleration at which the driver feels comfortable, a sd Denote the deceleration at which the driver feels comfortable, t rt Denote the reaction time of the driver; when the vehicle cannot pass through the intersection at the original speed in the current signal cycle and can pass through the intersection through acceleration guidance, generate a green light guidance model based on the acceleration guidance strategy:

[0162]

[0163] Among them, V t (j, k) denote the theoretical optimal speed of vehicle k in the coordinated phase of intersection j, L d (j, k) denote the distance of vehicle k in the coordinated phase of intersection j from the intersection, L s (j, k) the length of queue k of vehicle in the coordinated phase of intersection j, V a (j, k) denote the average driving speed of vehicle k in the coordinated phase of intersection j; when the vehicle cannot pass through by accelerating in the current signal cycle but can pass through by decelerating in the next cycle to achieve passing without stopping, generate a green light guidance model based on the deceleration guidance strategy:

[0164]

[0165] Among them, t(j, x + 1) denote the green light start time of the coordinated phase of intersection j in the (x + 1)-th cycle, t n Denote the current time; when the vehicle travels to the end of the queue and the start-up fluctuation reaches the end of the queue, but the vehicle cannot pass through the intersection, generate a red light guidance model based on the deceleration guidance strategy:

[0166]

[0167] Among them, It represents the speed at which the saturated traffic flow at intersection j passes through the intersection. A delay minimization model is constructed based on the speed guidance range, the green light guidance model, and the red light guidance model:

[0168]

[0169] Among them, D(j) represents the total delay at intersection j on the path, and L(j) represents the delay detection area at intersection j. It represents the average driving speed of the remaining sections except the guidance area in the delay detection area at intersection j. U(j) represents the number of passing vehicles, and v f represents the free flow speed.

[0170] The solution module uses a multi-objective based locust optimization algorithm to solve the green wave bandwidth maximization model and the delay minimization model to obtain the signal timing plan.

[0171] The working process of the solution module includes: randomly generating a population consisting of m locust individuals, each locust representing a potential traffic signal adjustment plan, and initializing the algorithm parameters at the same time; checking the constraint conditions for each locust individual in the population. If it is judged that the signal plan of a locust individual does not meet the traffic rules or safety requirements, the non-compliant individual is removed from the population; for each locust individual, based on the green wave bandwidth maximization model and the delay minimization model, calculate the fitness, and according to the Pareto domination principle, identify the non-dominated solution set, and evaluate and sort the non-dominated solution set; select the locust individual with the best distribution density in the non-dominated solution set as the global optimal solution Gbest for this iteration, and determine the current position of this individual as the historical optimal position of the individual; according to the global optimal solution Gbest and the historical optimal positions of each locust individual, adjust the weight coefficient of the algorithm, update the position and speed of each locust individual; if the current position of the locust individual is better than the historical optimal position, replace the historical optimal position with the current position, select the global optimal solution according to the optimal density distance, and update the global optimal solution Gbest. If the fitness of the global optimal solution Gbest is higher than the previous optimal fitness at this time, add this Gbest to the new non-dominated solution set and remove the dominated individuals in the solution set; detect whether the preset maximum number of iterations is reached, or whether the accuracy of the locust individuals meets the set requirements. If either condition is satisfied, output the maximum domination degree Pareto solution of the corresponding fitness value and its corresponding signal timing plan.

[0172] Embodiment III

[0173] In this embodiment, through case analysis, the effectiveness of the present invention is verified.

[0174] In order to verify the effect of the model, four consecutive intersections of Xingguang Road and Haitai North Road-Ziyang Road-Huikang Road-Zhongbei Avenue in Tianjin were selected as the research objects. There are dense residential communities and many office buildings along this section of road, which is an important OD point for commuter traffic. The main road of this section is a two-way 4-lane road, and the distances between adjacent intersections are 628, 580, and 862 meters.

[0175] The peak hour traffic flow at each intersection along the line is collected. The survey period is the morning peak time on September 5, 2024: 7:00-8:00. The flow direction of each intersection is shown in Table 1.

[0176] Table 1

[0177]

[0178] The Webster formula was used to solve the optimal cycle of each intersection, and it was found that intersection ③ was the key intersection, and the optimal cycle was 150s; the cycles of intersections ① and ④ were also adjusted to 150s, and the cycle of intersection ② was adjusted to 75s. The green light display time of each intersection was obtained, as shown in Table 2.

[0179] Table 2

[0180]

[0181] Based on relevant literature and field survey flow data, the following four key paths are determined. The path information is as follows: Figure 2 As shown in Table 3. According to the traffic volume, Path 1 and Path 3 can be regarded as high-flow paths, and Path 2 and Path 4 are low-flow paths. High-flow paths are more important, as they bear greater traffic pressure and play a key role in the smooth operation of the entire traffic network. Low-flow paths can be used as auxiliary when necessary to share part of the traffic pressure for high-flow paths.

[0182] Table 3

[0183]

[0184] This embodiment optimizes the signal timing scheme, sets the upper limit of the cycle time to 180 seconds, the lower limit to 60 seconds, the shortest green light time to 10 seconds, and the green light interval to 3 seconds. Based on the multi-objective locust optimization algorithm, the maximum number of iterations t is set to 100 times, the population size m is set to 200, and the population random variable rand is initialized to a value between (0,1). l Set to 0.8, G l , A lBoth are set to 0.1. The increment coefficient is 1, and the decrement coefficient is 0.00001. The gravitational constant and the wind direction constant are 1. To fully demonstrate the superiority of the model proposed in this paper in the field of multi-path arterial signal coordination control, in this embodiment, the multi-path model proposed by Yang et al. (abbreviated as Yang-M2) is selected for comparative analysis.

[0185] It can be seen from the coordination scheme of the Yang-M2 model that the cumulative green wave bandwidth of each path is 107 seconds. It can be seen from the coordination scheme of the present invention that the cumulative green wave bandwidth increases to 110 seconds. Compared with the Yang-M2 model, due to the reallocation of bandwidth, although the green wave bandwidth of sections ①-② and ②-③ decreases, the overall cumulative green wave bandwidth increases by 3 seconds. This is mainly achieved by preferentially optimizing high-flow intersections, and the bandwidth of each path on section ③-④ is significantly increased (by 8 seconds, 6 seconds, and 6 seconds).

[0186] Analysis of simulation verification results:

[0187] First, a simulation scenario is established using Vissim. To verify the effectiveness of the model proposed in the present invention, simulations are respectively carried out on the Webster timing scheme, the Yang-M2 scheme, and the optimized model scheme of the present invention, and comparative analysis is performed based on the simulation results.

[0188] In the research on the optimized model, according to the vehicle flow situation, the length of the speed guidance area is determined to be 180m, the length of the queuing area is 60m, the maximum vehicle guidance speed is 17m / s, the propagation speed of the starting wave is 4.5m / s, and the maximum acceleration is 2m / s 2 , and the maximum deceleration is -2m / s 2 ; the saturated flow rate of the straight lane is 1650 pcu / h, and the saturated flow rate of other approach lanes is 1550 pcu / h; the standard headway is 2s, and the driver's reaction time is 1.5s; the load threshold δ is set to 0.7.

[0189] Set the simulation parameters: Taking 4500 seconds as the simulation cycle, considering the instability in the initial stage of the simulation, the first 900 seconds are set as the simulation warm-up period, and the remaining 3600 seconds of simulation duration are used for experimental analysis. For the key paths along the line, the comparison results of each index value before and after optimization are shown in Table 4.

[0190] Table 4

[0191]

[0192] It can be seen that, compared with the Yang-M2 scheme, the proposed scheme in this study shows significant advantages in multiple paths. For example, the average vehicle delay on Path 1 is reduced by 4.82 seconds (about 4%), on Path 3 by 14.89 seconds (about 12.5%), and on Path 4 by 7.2 seconds (about 7%), indicating that the optimization effect is particularly obvious in high-traffic sections. In terms of the average vehicle stop times, on Path 1 it is reduced by 0.09 times, on Path 3 by 0.21 times, and on Path 4 by 0.12 times, further improving the traffic efficiency. However, on Path 2, although the average vehicle delay of the proposed scheme in this study is lower than that of the Webster scheme, it is still 8.39 seconds higher than that of the Yang-M2 scheme. This is because the green wave band is reduced due to path competition, and the signal timing cannot be fully coordinated. In addition, although the average vehicle stop times on Path 2 have decreased, they are still higher than those of the Yang-M2 scheme, indicating that there is still room for improvement in the green wave optimization on low-traffic paths.

[0193] To further consider the impact of the model proposed in this invention on vehicles outside the 4 key paths, an overall analysis is carried out on the vehicles at all intersections on the arterial road. To test the overall effect of the optimization scheme, the average vehicle delay and the average vehicle stop times are used as evaluation indicators of the simulation results. The simulation results are shown in Table 5.

[0194] Table 5

[0195]

[0196] It can be seen from this that, compared with the classical Webster model, the optimized model in this study reduces the average vehicle delay by about 10.75%, the average stop times by about 43.26%, and the average travel time by about 10.84%; compared with the Yang-M2 model, the average vehicle delay is reduced by about 6.53%, the average stop times increase by about 16.44%, and the average travel time increases by about 3.69%. This shows that this invention has significant advantages in improving commuting efficiency.

[0197] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for multi-path coordinated control of urban roads based on an improved AM-Band model, characterized in that: The following steps are involved: Based on the path flow and turning requirements, the original AM-Band model is improved to obtain the maximum green wave bandwidth model for multi-path. Based on the indicator light signal devices between the upstream and downstream intersections, the speed guidance range is determined, and a delay minimization model based on optimal speed guidance is established; The green wave bandwidth maximum model and the delay minimization model are solved by using a multi-objective locust optimization algorithm to obtain a signal timing plan.

2. According to claim 1, a method for coordinated control of urban road multi-paths based on an improved AM-Band model, characterized in that: The method for constructing the maximum green wave bandwidth model includes: The original AM-Band model is improved to obtain the multipath green wave objective function: Where Z represents the sum of weighted green wave bandwidths of uplink and downlink key paths of all intersections on the entire road section of the optimization target. represents the weight of the upward direction of path i at intersection j, represents the left half of the green wave bandwidth of path i on intersection j in the uplink direction, represents the right half of the green wave bandwidth of path i on intersection j in the uplink direction, represents the weight of the downlink direction of path i at intersection j, represents the left half of the green wave bandwidth of path i at intersection j in the downlink direction, represents the right half of the green wave bandwidth of path i on intersection j in the downlink direction, M represents the total number of intersections, N j represents the number of critical paths on intersection j; Several constraints are imposed on the multipath green wave objective function to obtain the maximum green wave bandwidth model.

3. According to claim 1, a method for coordinated control of urban road multi-paths based on an improved AM-Band model, characterized in that: The method of constructing the delay minimization model includes: Determine the speed guidance range: Among them, L c Indicates the speed guidance area control range, V max Indicates the maximum speed of the road section, V min Indicates the minimum speed of the road section, a sp Indicates the acceleration that the driver feels comfortable with, a sd Indicates the deceleration that the driver feels comfortable with, t rt Indicates the driver's reaction time; When a vehicle cannot pass through the intersection at its original speed during the current signal cycle but can pass through the intersection through acceleration guidance, a green light guidance model is generated based on the acceleration guidance strategy: Among them, V t (j,k) represents the theoretical optimal speed of vehicle k in the coordinated phase of intersection j, L d (j,k) represents the distance between vehicle k in the coordinated phase j and the intersection, L s (j,k) The length of vehicle k in the coordinated phase queue at intersection j, V a (j,k) represents the average speed of vehicles in the coordination phase j of the intersection; The vehicle cannot accelerate through the current signal cycle, but can decelerate through the next cycle, thus achieving non-stop passage. The green light guidance model is generated based on the deceleration guidance strategy: Where t(j,x+1) represents the green light turn-on time of intersection j in the coordination phase of x+1 cycle, t n Indicates the current moment; When the vehicle reaches the end of the queue and the start-up fluctuation is transmitted to the end of the queue, but the vehicle cannot pass through the intersection, a red light guidance model is generated based on the deceleration guidance strategy: Among them, V j s represents the speed of the saturated traffic flow at intersection j passing through the intersection; The delay minimization model is constructed based on the speed guidance range, the green light guidance model and the red light guidance model: Where D(j) represents the total delay at intersection j on the path, L(j) represents the delay detection area at intersection j, represents the average speed of the remaining sections of the delay detection area of ​​intersection j except the guidance area, U(j) represents the number of vehicles passing through, and v f Indicates free flow speed.

4. According to claim 1, a method for coordinated control of urban road multi-paths based on an improved AM-Band model, characterized in that: The method for solving the green wave bandwidth maximum model and the delay minimization model by using the locust optimization algorithm includes: A group of m locust individuals is randomly generated, each locust represents a potential traffic signal adjustment scheme, and the algorithm parameters are initialized; Constraint checks are performed on each locust individual in the group. If it is determined that the signal scheme of a locust individual does not meet traffic rules or safety requirements, the individual that does not meet the requirements will be removed from the group. For each locust individual, the fitness is calculated based on the green wave bandwidth maximum model and the delay minimization model, and a non-inferior solution set is identified according to the Pareto dominance principle, and the non-inferior solution set is evaluated and ranked; The locust individual with the best distribution density in the non-inferior solution set is selected as the global optimal solution Gbest of this iteration, and the current position of the individual is determined as the historical optimal position of the individual; According to the global optimal solution Gbest and the historical optimal position of each locust individual, the weight coefficient of the algorithm is adjusted to update the position and speed of each locust individual; If the current position of the locust individual is better than the historical optimal position, the historical optimal position is replaced by the current position, the global optimal solution is selected according to the optimal density distance, and the global optimal solution Gbest is updated. If the fitness of the global optimal solution Gbest is higher than the previous optimal fitness, Gbest is added to the new non-inferior solution set, and the dominated individuals in the solution set are removed; Detect whether the preset maximum number of iterations is reached, or whether the accuracy of the locust individuals meets the set requirements. If any condition is met, output the maximum dominance Pareto solution corresponding to the fitness value and the corresponding signal timing plan.

5. A multi-path coordinated control system for urban roads based on an improved AM-Band model, the system applying the method described in any one of claims 1 to 4, characterized in that: include: A first model building module, a second model building module and a solution solving module; The first model building module improves the original AM-Band model based on the path flow and turning requirements to obtain a multi-path oriented green wave bandwidth maximum model; The second model building module determines the speed guidance range based on the indicator light signal device between the upstream and downstream intersections, and establishes a delay minimization model based on the optimal speed guidance; The solution solving module uses a locust optimization algorithm based on multiple objectives to solve the green wave bandwidth maximum model and the delay minimization model to obtain a signal timing solution.

6. According to claim 5, a multi-path coordinated control system for urban roads based on an improved AM-Band model is characterized in that: The workflow of the first model building module includes: The original AM-Band model is improved to obtain the multipath green wave objective function: Where Z represents the sum of weighted green wave bandwidths of uplink and downlink key paths of all intersections on the entire road section of the optimization target. represents the weight of the upward direction of path i at intersection j, represents the left half of the green wave bandwidth of path i on intersection j in the uplink direction, represents the right half of the green wave bandwidth of path i on intersection j in the uplink direction, represents the weight of the downlink direction of path i at intersection j, represents the left half of the green wave bandwidth of path i at intersection j in the downlink direction, represents the right half of the green wave bandwidth of path i on intersection j in the downlink direction, M represents the total number of intersections, N j represents the number of critical paths on intersection j; Several constraints are imposed on the multipath green wave objective function to obtain the maximum green wave bandwidth model.

7. According to claim 5, a multi-path coordinated control system for urban roads based on an improved AM-Band model is characterized in that: The workflow of the second model building module includes: Determine the speed guidance range: Among them, L c Indicates the speed guidance area control range, V max Indicates the maximum speed of the road section, V min Indicates the minimum speed of the road section, a sp Indicates the acceleration that the driver feels comfortable with, a sd Indicates the deceleration that the driver feels comfortable with, t rt Indicates the driver's reaction time; When a vehicle cannot pass through the intersection at its original speed during the current signal cycle but can pass through the intersection through acceleration guidance, a green light guidance model is generated based on the acceleration guidance strategy: Among them, V t (j,k) represents the theoretical optimal speed of vehicle k in the coordinated phase of intersection j, L d (j,k) represents the distance between vehicle k in the coordinated phase j and the intersection, L s (j,k) The length of vehicle k in the coordinated phase queue at intersection j, V a (j,k) represents the average speed of vehicles in the coordination phase j of the intersection; The vehicle cannot accelerate through the current signal cycle, but can decelerate through the next cycle, thus achieving non-stop passage. The green light guidance model is generated based on the deceleration guidance strategy: Where t(j,x+1) represents the green light turn-on time of intersection j in the coordination phase of x+1 cycle, t n Indicates the current moment; When the vehicle reaches the end of the queue and the start-up fluctuation is transmitted to the end of the queue, but the vehicle cannot pass through the intersection, a red light guidance model is generated based on the deceleration guidance strategy: in, represents the speed of the saturated traffic flow at intersection j passing through the intersection; The delay minimization model is constructed based on the speed guidance range, the green light guidance model and the red light guidance model: Where D(j) represents the total delay at intersection j on the path, L(j) represents the delay detection area at intersection j, represents the average speed of the remaining sections of the delay detection area of ​​intersection j except the guidance area, U(j) represents the number of vehicles passing through, and v f Indicates free flow speed.

8. According to claim 5, a multi-path coordinated control system for urban roads based on an improved AM-Band model is characterized in that: The workflow of the solution-solving module includes: A group of m locust individuals is randomly generated, each locust represents a potential traffic signal adjustment scheme, and the algorithm parameters are initialized; Constraint checks are performed on each locust individual in the group. If it is determined that the signal scheme of a locust individual does not meet traffic rules or safety requirements, the individual that does not meet the requirements will be removed from the group. For each locust individual, the fitness is calculated based on the green wave bandwidth maximum model and the delay minimization model, and a non-inferior solution set is identified according to the Pareto dominance principle, and the non-inferior solution set is evaluated and ranked; The locust individual with the best distribution density in the non-inferior solution set is selected as the global optimal solution Gbest of this iteration, and the current position of the individual is determined as the historical optimal position of the individual; According to the global optimal solution Gbest and the historical optimal position of each locust individual, the weight coefficient of the algorithm is adjusted to update the position and speed of each locust individual; If the current position of the locust individual is better than the historical optimal position, the historical optimal position is replaced by the current position, the global optimal solution is selected according to the optimal density distance, and the global optimal solution Gbest is updated. If the fitness of the global optimal solution Gbest is higher than the previous optimal fitness, Gbest is added to the new non-inferior solution set, and the dominated individuals in the solution set are removed; Check whether the preset maximum number of iterations is reached, or whether the accuracy of the locust individuals meets the set requirements. If any condition is met, the maximum dominance Pareto solution of the corresponding fitness value and its corresponding signal timing scheme are output.

Images