Dynamic traffic distribution and signal control combined optimization method

Abstract

The invention discloses a dynamic traffic distribution and signal control combined optimization method, and belongs to the field of intelligent traffic. The method specifically comprises the steps that firstly, a road section impedance function is constructed, node impedance is introduced to obtain an intersection impedance combination function, and the intersection impedance combination function is optimized through the traffic flow, density and speed relation based on dynamic adjustment parameters, vehicle type differences, weather conditions and traffic flow speed to obtain a combination optimization traffic impedance model; and solving the combinatorial optimization traffic impedance model by using a particle swarm algorithm to output a traffic distribution scheme. According to the method, dynamic traffic distribution and adaptive signal control are integrated, the road network emergency response capability is improved, traffic prediction and signal regulation and control synchronization is realized, and an accurate impedance model is introduced to dynamically adapt to driver preferences, so that congestion is relieved, and travel time and resource allocation are optimized.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and in particular relates to a dynamic traffic allocation and signal control combined optimization method. Background Art

[0002] Traffic distribution and signal control are two crucial aspects of urban traffic management. They can effectively reduce traffic congestion and improve the operating efficiency of the overall road network. Traffic distribution mainly guides travelers to make path choices and optimizes the traffic distribution in the entire road network based on the actual travel time of each road section; while signal control adjusts the timing of traffic lights based on these traffic distributions, which directly affects the travel time of the road section, and then prompts travelers to reconsider their path choices. Both methods are closely related to the two key factors of traffic distribution and travel time, and their synergy determines the overall performance of the urban road network. Through precise traffic distribution and intelligent signal control strategies, a more balanced traffic distribution can be achieved, traffic bottlenecks can be reduced, and the goal of improving the efficiency of the urban transportation system can be achieved.

[0003] In the field of transportation planning, traditional traffic allocation plays a core role. As the last link in the transportation planning process, its purpose is to reasonably allocate the traffic demand based on the travel occurrence and attraction (OD) matrix to the road network to predict the future traffic volume on the road. This process is crucial to the overall transportation planning, because transportation planning aims to conduct in-depth analysis and prediction of future transportation demand based on historical and current traffic supply and demand, which is a long-term and complex task.

[0004] In contrast, signal control focuses on using the current road network flow distribution data to achieve real-time signal timing adjustments to alleviate traffic congestion, and its nature is more inclined to short-term operations. Despite this, there is a mismatch between traditional traffic distribution and signal control in terms of time scale. The former serves long-term planning, while the latter focuses on immediate response. However, with the advancement of technology, traffic distribution, as an effective tool to guide travelers' path selection, the flow distribution it forms is not only suitable for long-term traffic network planning, but can also be applied to real-time traffic operation management and regulation. This means that traffic distribution and signal control can work together to optimize the overall travel cost of the road network.

[0005] However, traffic assignment under the existing technical framework still faces some challenges, including but not limited to insufficient adaptability to dynamic traffic flow changes, failure to fully consider changes in traveler behavior, and difficulty in achieving seamless integration with signal control systems. Therefore, in order to fully tap the potential of these two methods, further research is needed on how to better integrate traffic assignment and signal control so that they can effectively support traffic planning and operations at different time scales. Summary of the invention

[0006] The purpose of the embodiment of the present invention is to provide a combined optimization method of dynamic traffic allocation and signal control, which combines dynamic traffic allocation with signal control to provide a real-time changing signal timing plan to reduce traffic congestion.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is a combined optimization method of dynamic traffic assignment and signal control, comprising the following steps:

[0008] The road section impedance function is constructed, and the node impedance is introduced to obtain the intersection impedance combination function. Based on the dynamic adjustment parameters, vehicle type differences, weather conditions, traffic flow speed, and the relationship between traffic flow, density and speed, the intersection impedance combination function is optimized to obtain a combined optimization traffic impedance model; and the particle swarm algorithm is used to solve the combined optimization traffic impedance model to output the traffic allocation plan.

[0009] Furthermore, constructing the road section impedance function includes constructing a progressive impedance function:

[0010]

[0011] Among them, t i The traffic volume of the road section is q i The travel time of the road section at t 0 It indicates the time that the vehicle travels through the road section at the free flow speed; q i Indicates the traffic volume of the road section; c i represents the road section capacity; J represents the service level parameter.

[0012] Furthermore, the node impedance is represented by the signal-controlled intersection delay; the signal-controlled intersection delay is specifically determined by the balanced phase average delay and the random average delay, and the specific process is:

[0013] Establishing the average delay d of the balanced phase e :

[0014]

[0015] Among them, C represents the length of the intersection signal cycle, λ represents the green signal ratio, and x represents the traffic saturation;

[0016] Establish the random average delay d r :

[0017]

[0018] Among them, q represents the vehicle arrival rate, which means the number of vehicles arriving per unit time;

[0019] Based on the expressions of balanced phase average delay and random average delay, the correction term of random delay is established to determine and simplify the signal control intersection delay d:

[0020]

[0021] Furthermore, the intersection impedance combination function I 0 Specifically:

[0022]

[0023] Among them, Q represents the actual traffic volume passing through the road section; α and β are the unknown parameters of the model, t i0 is the free flow travel time of traffic on section i, q represents the vehicle arrival rate, C represents the intersection signal cycle length, λ represents the green signal ratio, and x represents the traffic saturation.

[0024] Furthermore, the specific optimization process of the intersection impedance combination function is as follows:

[0025] S2031. Optimize the road section impedance function: modify the road section impedance function, and the relationship between the three elements of traffic flow is as follows:

[0026] Q=VK

[0027] Where Q represents the actual traffic volume passing through the road section, V represents the average speed of vehicles passing through the road section, and K represents the number of vehicles per unit section length;

[0028] S2032. Determine the relationship between flow velocity V and density K:

[0029]

[0030] Among them, K j is the traffic flow density under congestion conditions, V f is the free-flow speed;

[0031] S2033. Incorporating the relationship between flow, density, and speed, and considering other factors that affect road impedance: weather, lane width, and lane type, the improved road impedance function is as follows:

[0032]

[0033] Among them, I 1 is the improved road impedance function; γ is the density influencing factor; δ is the nonlinear density coefficient; η is the speed influencing factor; φ is the weather influencing coefficient; W is the weather condition parameter, reflecting the impact of weather on traffic flow; ψ is the lane width influencing coefficient; L is the lane width; θ is the lane type parameter; T is the lane type; α and β represent the parameters to be determined in the model; t 0It indicates the time that vehicles travel through the road segment at free flow speed;

[0034] S2034. Considering external objective factors such as weather and pedestrians, and combining the driver's personal decision-making factors, the improved intersection impedance combination function is optimized:

[0035]

[0036] Among them, I 2 is the intersection impedance function; σ 1 and σ 2 is a nonlinear parameter; P is the pedestrian influence factor; S is the driver's personal influence factor; W is the weather condition parameter; τ are the influencing parameters of pedestrian and driver behaviors respectively;

[0037] S2035. Considering the different effects of the two types of impedance on drivers' driving, combined with the weight parameter η, the final combined optimization traffic impedance model I is determined:

[0038]

[0039] Among them, η represents the weight parameter.

[0040] Furthermore, the application of the particle swarm algorithm to solve the combined optimization traffic impedance model to output the traffic assignment scheme is specifically:

[0041] S301, initializing the population: assigning a random initial traffic allocation flow to each particle in the population;

[0042] S302, calculate the fitness value: calculate the total system travel time according to the combined optimization traffic impedance model, and then calculate the fitness value of each particle in the current iteration according to the objective function of the shortest total system travel time. The fitness value of each particle represents the total travel time of the current traffic flow distribution result. The position searched by the particle is taken as the local optimal position, and the optimal position searched by the particle population is taken as the global optimal position.

[0043] S303, particle attribute update: update the speed and position of the particle, and update the position and speed of the particle according to the speed and position update formula;

[0044] S304, updating the current optimal solution position: recalculating the fitness value of each particle, and then comparing it with the fitness value corresponding to the local optimal position; if the fitness value of a particle is better than the fitness value of the local optimal position, the position of the particle is used as the new local optimal position;

[0045] S305, updating the global optimal solution position: comparing the fitness value of each particle with the fitness value corresponding to the global optimal position. If the fitness value of a particle is better than the fitness value of the global optimal position, the position of the particle is used as the new global optimal position.

[0046] S306, judging whether the particle has reached the optimum or whether the maximum number of iterations has been reached; if the maximum number of iterations has been reached, executing S307, otherwise executing S302;

[0047] S307: Output the optimal traffic signal timing scheme in different time periods and the optimal flow distribution in the same time period.

[0048] Furthermore, the objective function min I for the shortest travel time in S302 is

[0049] min I=ηI 1 +(1-η)I 2

[0050] Among them, η represents the weight parameter, I 2 is the improved intersection impedance function, I 1 is the improved section impedance function.

[0051] Furthermore, the speed and position update formula in S303 is specifically:

[0052] Speed ​​update formula:

[0053]

[0054] The position update formula is:

[0055]

[0056] in, is the velocity vector of particle i in the kth iteration with the dth dimension, is the position vector of particle i in the kth iteration with dimension d, is the individual historical optimal position of particle i in the dth dimension in the kth iteration, is the global historical optimal position of the dth dimension of the group in the kth iteration; ω is the inertia weight, which makes the particle move a certain distance at the original speed, c 1 and c 2 is the social cognition coefficient, c 1 indicates that the particle moves according to its own experience, c 2 Indicates that particles move according to global information, which is called the social cognitive coefficient. Particles adjust according to the global optimal position found in the group. These two parameters are used for speed update in the formula. By adjusting c 1 and c2 The value of can balance the particle's dependence on individual experience and global information. In the calculation of the formula, these two coefficients determine whether the particle relies more on its own experience or global information; r 1 and r 2 is a random number in the interval [0,1].

[0057] Furthermore, a linearly decreasing inertia weight is used instead of a fixed inertia weight:

[0058]

[0059] Among them, ω max is the maximum inertia weight, ω min is the minimum inertia weight, k is the current iteration number, k max is the maximum number of iterations.

[0060] Compared with the prior art, the beneficial effects of the present invention include the following points:

[0061] Enhanced dynamic response performance: By integrating a dynamic traffic allocation algorithm with an adaptive signal control system, the present invention can instantly adjust the cycle and phase of traffic lights when faced with sudden traffic conditions (such as accidents or abnormal traffic flow increases), thereby significantly improving the rapid response capability and adaptability of the entire road network to sudden events, thereby effectively alleviating traffic congestion.

[0062] Achieving multi-time scale consistency: Through the design of the refined optimization algorithm, the present invention achieves the synchronous operation of traffic demand forecasting and signal control mechanism in the same time dimension, ensuring that the two can work together seamlessly. This synchronization process helps to comprehensively optimize travel time in the road network, thereby improving the operating efficiency of the entire transportation system.

[0063] Reflecting the dynamic evolution of travel decisions: The present invention introduces a more accurate impedance model to describe the state of the traffic network, which can not only accurately capture the changes in drivers' path selection preferences under different conditions, but also dynamically update these preferences based on real-time data, thereby making the traffic flow distribution closer to the actual situation and promoting the effective allocation of traffic resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1Schematic diagram of the combined optimization process of dynamic traffic assignment and signal control according to an embodiment of the present invention;

[0066] Figure 2 is the change of the flow rate allocated to each path under different signal cycles in the embodiment of the present invention;

[0067] Figure 3 is a graph showing changes in the distribution flow of paths under different cycle lengths according to an embodiment of the present invention;

[0068] Figure 4 This is a diagram of impedance changes of three paths after considering intersection delays according to an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] like Figure 1 This embodiment provides a combined optimization method of dynamic traffic assignment and signal control, specifically, combining the road section impedance function and the intersection delay combination function, and applying the particle swarm algorithm to solve the combined optimization model. In some specific embodiments, dynamic traffic assignment refers to the reasonable allocation of time-varying traffic trips to different paths, the reasonable configuration of people's existing traffic needs, and the high-quality and efficient operation of the traffic network.

[0071] In this embodiment, the optimization method is to perform iterative optimization (IOA), system optimization (SOP) and hybrid optimization (HOP) on Cournot game, monopoly game and master-slave game respectively. The main difference between the three optimization problems is the different connections between traffic managers and road travelers and the different connections between road travelers.

[0072] Example 1

[0073] like Figure 1 , a dynamic traffic assignment and signal control combined optimization method, specifically comprising the following steps:

[0074] S1, construction of road section impedance function;

[0075] Road impedance plays a central role in traffic flow assignment models, as it quantifies the time cost experienced by traffic participants when passing through a road network. Road impedance not only indirectly reflects the congestion level and capacity of the road network, but is also a key factor in determining the path selection behavior of traffic participants.

[0076] The road section impedance function is a function that reflects the relationship between the road section impedance and the traffic demand, and is the key to the dynamic urban traffic flow distribution. The impedance function of the progressive characteristic (in this embodiment, the progressive characteristic refers to the characteristic exhibited by a system or model when it gradually approaches or approaches a certain state or result; that is, the process in which the impedance function gradually changes with the change of traffic flow, that is, when the traffic flow approaches a certain critical value, the change speed of the impedance may slow down and gradually tend to a stable value or limit value) established in this embodiment is:

[0077]

[0078] Among them, t i The traffic volume of the road section is q i The travel time of the road section at t 0 It indicates the time that the vehicle travels through the road section at the free flow speed; q i Indicates the traffic volume of the road section; c i represents the road section capacity; J represents the service level parameter.

[0079] S2, construction of intersection impedance combination function considering intersection delay;

[0080] This embodiment calculates the delay by delay analysis method, thereby obtaining the node impedance function. The signal control intersection delay includes two parts: balanced phase average delay and random average delay.

[0081] S201. The average delay in the equilibrium phase assumes that the average arrival rate and traffic capacity of each entrance lane remain stable throughout the entire time period. The vehicle is interrupted by the control of the traffic light during the driving process. It is an inevitable delay for the vehicle. The specific expression is:

[0082]

[0083] Among them, C represents the length of the intersection signal cycle, λ represents the green signal ratio, and x represents the traffic saturation. In the formula of the average delay of the equilibrium phase, the equilibrium average delay time d e It increases with the increase of the import channel signal period C, and decreases with the increase of the import channel green-to-signal ratio λ.

[0084] S202. The average delay of the balanced phase is obtained under the assumption of a stable arrival rate. In fact, the vehicle arrival rate fluctuates randomly between one signal cycle and another signal cycle. In order to take into account these additional delays, this embodiment establishes a random average delay:

[0085]

[0086] Among them, q represents the vehicle arrival rate, which means the number of vehicles arriving per unit time. Based on the expressions of equilibrium phase average delay and random average delay, the correction term of random delay is established to obtain the delay of signalized intersection:

[0087]

[0088] Among them, it was later found that the value of the third random delay correction term accounted for 5% to 15% of the entire formula, so the simplified signal control intersection delay was obtained as:

[0089]

[0090] The intersection impedance in urban roads is composed of two parts: section impedance and node impedance. The node impedance is represented by the signal-controlled intersection delay. By adding the intersection delay parameter to the section impedance function, the intersection impedance combination function can be obtained as follows:

[0091]

[0092] Among them, Q represents the actual traffic volume passing through the road section; α and β are the unknown parameters of the model, t i0 is the free flow travel time of vehicles passing through road section i.

[0093] S203. In order to better simulate the impedance of traffic infrastructure, this implementation method takes into account dynamic adjustment parameters, vehicle type differences, weather conditions, and traffic flow speed, and optimizes equation (6) through the relationship between traffic flow, density, and speed.

[0094] S2031. Optimize the road section impedance function: The original road section impedance function cannot reflect the relationship between the three elements of road urban traffic flow, namely, the relationship between flow (Q), density (K) and speed (V). In order to better simulate the traffic flow, the road section impedance function is modified. The relationship between the three elements of traffic flow is as follows:

[0095] Q=VK(7)

[0096] Among them, Q represents the actual traffic volume passing through the section, V represents the average speed of the interval, that is, the average speed of vehicles passing through the section, and K represents the density of vehicles on the road, that is, the number of vehicles per unit section length.

[0097] S2032. Determine the relationship between flow velocity V and density K:

[0098]

[0099] Among them, K j is the traffic flow density under congestion conditions, V f is the free flow speed.

[0100]

[0101] S2033. Incorporating the relationship between flow, density, and speed, and considering other factors that affect road impedance such as weather, lane width, and lane type, the improved road impedance function is as follows:

[0102]

[0103] Among them, I 1 is the improved road impedance function; γ is the density influencing factor; δ is the nonlinear density coefficient; η is the speed influencing factor; φ is the weather influencing coefficient; W is the weather condition parameter, reflecting the impact of weather on traffic flow; ψ is the lane width influencing coefficient; L is the lane width; θ is the lane type parameter; T is the lane type; α and β represent the parameters to be determined in the model; t 0 Represents the time that vehicles travel through the road segment at free-flow speed.

[0104] S2034. Optimization of the improved intersection impedance combination function: When crossing an intersection, the driver is not only affected by the external objective environment, but also by the driver's decision-making behavior. Considering external objective factors such as weather and pedestrians, and combining the driver's personal decision-making factors, the optimized intersection impedance combination function is as follows:

[0105]

[0106] Among them, I 2 is the improved intersection impedance function; σ 1 and σ 2 is a nonlinear parameter; P is the pedestrian influence factor; S is the driver's personal influence factor; W is the weather condition parameter; τ are the influencing parameters of pedestrian and driver behaviors respectively.

[0107] S2035. Considering the different effects of the two types of impedance on drivers' driving, combined with the weight parameter η, the final combined optimization traffic impedance model I is determined:

[0108]

[0109] Among them, η represents the weight parameter, which is used to adjust these two types of impedance (I 1 and I 2 )’s weight in traffic impedance model I.

[0110] S3. Apply the particle swarm algorithm to solve the combinatorial optimization model. The particle swarm algorithm is initialized as a group of random particles, and then finds the optimal solution through continuous iteration. In each iteration, the particles update themselves through the individual optimal value and the global optimal value. After finding these two optimal values, the particles update their speed and position through the speed and position update formula. Speed ​​update formula:

[0111]

[0112] The position update formula is:

[0113]

[0114] in, is the velocity vector of particle i in the kth iteration with the dth dimension, is the position vector of particle i in the kth iteration with dimension d, is the individual historical optimal position of particle i in the dth dimension in the kth iteration, is the global historical optimal position of the swarm in the dth dimension in the kth iteration. ω is the inertia weight, which makes the particle maintain its original speed and move a certain distance. 1 is the social cognitive coefficient, which means that particles move according to their own experience, often called the individual cognitive coefficient, which guides particles to approach their historical optimal position; c 2 Indicates that particles move according to global information; in this embodiment, particles are adjusted according to the global optimal position found in the group. This social cognition coefficient is used for speed update in the formula. By adjusting c 1 and c 2 The value of can balance the particle's dependence on individual experience and global information. In the calculation of the formula, these two coefficients determine whether the particle relies more on its own experience or global information; r 1 and r 2 It is a random number in the interval [0,1], which increases the randomness of the search.

[0115] Learning factor c 1 and c 2 They represent the maximum movement of particles towards individual extreme values ​​and global extreme values ​​respectively. 1 A relatively large value should be taken. 2 Taking a relatively large value can balance the local and global search capabilities of ions. Therefore, the initialization of the inertia weight and the learning factor is particularly important. This implementation method uses a linearly decreasing inertia weight instead of a fixed inertia weight:

[0116]

[0117] Among them, ω maxis the maximum inertia weight, ω min is the minimum inertia weight, k is the current iteration number, k max is the maximum number of iterations. The inertia weight becomes smaller and smaller as the number of iterations increases, which is conducive to the movement of particles to the global optimal value in the later stage.

[0118] S301, initialization population:

[0119] Assign a random initial traffic allocation flow to each particle in the population;

[0120] S302, calculate the fitness value:

[0121] According to formula (12), the total system travel time is calculated, and then the fitness value of each particle in the current iteration is calculated according to the objective function of the shortest total system travel time. The fitness value of each particle represents the total travel time of the current traffic flow allocation result. The position searched by the particle is taken as the local optimal position, and the optimal position searched by the particle population is taken as the global optimal position. According to formula (12), the objective function is as follows:

[0122] min I=ηI 1 +(1-η)I 2 (16)

[0123] Among them, η represents the weight parameter, which is used to adjust these two types of impedance (I 1 and I 2 )’s weight in traffic impedance model I.

[0124] S303. Particle attribute update:

[0125] Update the speed and position of the particle according to the speed and position update formula.

[0126] S304, update the current optimal solution position:

[0127] Recalculate the fitness value of each particle, and then compare it with the fitness value corresponding to the local optimal position. If there is a particle that is better than the local optimal position, that is, the fitness value of the particle is better than the fitness value of the local optimal position, then the position of the particle is used as the new local optimal position.

[0128] S305, update the global optimal solution position:

[0129] Compare the fitness value of each particle with the fitness value corresponding to the global optimal position. If there is a particle that is better than the global optimal position, that is, the fitness value of a particle is better than the fitness value of the global optimal position, then the position of the particle is used as the new global optimal position.

[0130] S306: Determine whether the condition for ending iteration is met:

[0131] Determine whether the particle has reached the optimum or the maximum number of iterations; if the condition is met, execute step 307, otherwise execute step 302.

[0132] S307: Output the optimal traffic allocation plan of the model. Output the optimal traffic signal timing plan in different time periods and the optimal flow distribution in the same time period.

[0133] like Figure 2 This implementation integrates a dynamic traffic assignment algorithm with an adaptive signal control system, and can adjust the cycle and phase of traffic lights in real time when faced with emergencies (such as accidents or abnormal traffic increases), thereby significantly improving the entire road network's ability to respond to emergencies. The core of this is that the system can respond quickly and relieve congestion in a relatively short period of time.

[0134] like Figure 3 ,The optimization algorithm of this implementation ensures that traffic demand prediction and signal control are carried out simultaneously in the same time dimension, thereby maintaining stable performance in both short-term response and long-term planning. Figure 3 The effects of different signal cycle lengths on traffic flow were compared, and the results showed that even in different time periods, the system can maintain consistent traffic flow distribution and smaller delays by adjusting the cycle and signal timing.

[0135] like Figure 4 By introducing a more accurate impedance model, this implementation can capture the changes in the driver's path selection preferences under different traffic conditions. Figure 4 ,The experimental results show that after considering the intersection delay, the path impedance and ,flow distribution change dynamically, the driver’s path selection behavior becomes more ,reasonable, and the resource allocation efficiency is improved.

[0136] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A combined optimization method for dynamic traffic assignment and signal control, characterized in that: The following steps are involved: The road section impedance function is constructed, and the node impedance is introduced to obtain the intersection impedance combination function. Based on the dynamic adjustment parameters, vehicle type differences, weather conditions, traffic flow speed, and the relationship between traffic flow, density and speed, the intersection impedance combination function is optimized to obtain a combined optimization traffic impedance model; and the particle swarm algorithm is applied to solve the combined optimization traffic impedance model to output the traffic distribution plan.

2. A dynamic traffic assignment and signal control combined optimization method according to claim 1, characterized in that: The constructing of the road section impedance function includes constructing a progressive impedance function: Among them, t i The traffic volume of the road section is q i t0 is the time it takes for a vehicle to travel through a road section at free flow speed; q i Indicates the traffic volume of the road section; c i represents the road section capacity; J represents the service level parameter.

3. A combined optimization method for dynamic traffic assignment and signal control according to claim 1, characterized in that: The node impedance is represented by the signal-controlled intersection delay; the signal-controlled intersection delay is specifically determined by the balanced phase average delay and the random average delay, and the specific process is: Establishing the average delay d of the balanced phase e : Among them, C represents the length of the intersection signal cycle, λ represents the green signal ratio, and x represents the traffic saturation; Establish the random average delay d r : Among them, q represents the vehicle arrival rate, which means the number of vehicles arriving per unit time; Based on the expressions of balanced phase average delay and random average delay, the correction term of random delay is established to determine and simplify the signal control intersection delay d:

4. A dynamic traffic assignment and signal control combined optimization method according to claim 1, characterized in that: The intersection impedance combination function I0 is specifically: Among them, Q represents the actual traffic volume passing through the road section; α and β are the unknown parameters of the model, t i0 is the free flow travel time of traffic on section i, q represents the vehicle arrival rate, C represents the intersection signal cycle length, λ represents the green signal ratio, and x represents the traffic saturation.

5. A combined optimization method for dynamic traffic assignment and signal control according to any one of claims 1 or 4, characterized in that: The specific optimization process of the intersection impedance combination function is as follows: S2031. Optimize the road section impedance function: modify the road section impedance function, and the relationship between the three elements of traffic flow is as follows: Q=VK Where Q represents the actual traffic volume passing through the road section, V represents the average speed of vehicles passing through the road section, and K represents the number of vehicles per unit section length; S2032. Determine the relationship between flow velocity V and density K: Among them, K j is the traffic flow density under congestion conditions, V f is the free-flow speed; S2033. Incorporating the relationship between flow, density, and speed, and considering other factors that affect road impedance: weather, lane width, and lane type, the improved road impedance function is as follows: Among them, I1 is the improved road section impedance function; γ is the density influence factor; δ is the nonlinear density coefficient; η is the speed influence factor; φ is the weather influence coefficient; W is the weather condition parameter, reflecting the impact of weather on traffic flow; ψ is the lane width influence coefficient; L is the lane width; θ is the lane type parameter; T is the lane type; α and β represent the parameters to be determined in the model; t0 represents the time when the vehicle travels through the road section at the free flow speed; S2034. Considering external objective factors such as weather and pedestrians, and combining the driver's personal decision-making factors, the improved intersection impedance combination function is optimized: Among them, I2 is the intersection impedance function; σ1 and σ2 are nonlinear parameters; P is the pedestrian influence factor; S is the driver's personal influence factor; W is the weather condition parameter; τ are the influencing parameters of pedestrian and driver behaviors respectively; S2035. Considering the different effects of the two types of impedance on drivers' driving, combined with the weight parameter η, the final combined optimization traffic impedance model I is determined: Among them, η represents the weight parameter.

6. A dynamic traffic assignment and signal control combined optimization method according to claim 1, characterized in that: The application of particle swarm algorithm to solve the combined optimization traffic impedance model to output the traffic assignment scheme is specifically: S301, initializing the population: assigning a random initial traffic allocation flow to each particle in the population; S302, calculate the fitness value: calculate the total system travel time according to the combined optimization traffic impedance model, and then calculate the fitness value of each particle in the current iteration according to the objective function of the shortest total system travel time. The fitness value of each particle represents the total travel time of the current traffic flow distribution result. The position searched by the particle is taken as the local optimal position, and the optimal position searched by the particle population is taken as the global optimal position. S303, particle attribute update: update the speed and position of the particle, and update the position and speed of the particle according to the speed and position update formula; S304, updating the current optimal solution position: recalculating the fitness value of each particle, and then comparing it with the fitness value corresponding to the local optimal position; if the fitness value of a particle is better than the fitness value of the local optimal position, the position of the particle is used as the new local optimal position; S305, update the global optimal solution position: compare the fitness value of each particle with the fitness value corresponding to the global optimal position. If the fitness value of a particle is better than the fitness value of the global optimal position, the position of the particle is updated. As the new global optimal position; S306, judging whether the particle has reached the optimum or whether the maximum number of iterations has been reached; if the maximum number of iterations has been reached, executing S307, otherwise executing S302; S307: Output the optimal traffic signal timing scheme in different time periods and the optimal flow distribution in the same time period.

7. A combined optimization method for dynamic traffic assignment and signal control according to claim 6, characterized in that: The objective function min I for the shortest travel time in S302 is: min I=ηI1+(1-η)I2 Among them, η represents the weight parameter, I2 is the improved intersection impedance function, and I1 is the improved road section impedance function.

8. A combined optimization method for dynamic traffic assignment and signal control according to claim 6, characterized in that: The speed and position update formula in S303 is as follows: Speed ​​update formula: The position update formula is: in, is the velocity vector of particle i in the kth iteration with the dth dimension, is the position vector of particle i in the kth iteration with dimension d, is the individual historical optimal position of particle i in the dth dimension in the kth iteration, is the global historical optimal position of the dth dimension of the group in the kth iteration; ω is the inertia weight, which makes the particle maintain the original speed and move a certain distance; c1 and c2 are social cognitive coefficients, c1 means that the particle moves according to its own experience, c2 means that the particle moves according to global information, r1 and r2 are random numbers in the interval [0,1].

9. A combined optimization method for dynamic traffic assignment and signal control according to claim 8, characterized in that: Use a linearly decreasing inertia weight instead of a fixed inertia weight: Among them, ω max is the maximum inertia weight, ω min is the minimum inertia weight, k is the current iteration number, k max is the maximum number of iterations.

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