CN113920760B - Traffic signal lamp timing optimization method considering complex microenvironment characteristics - Google Patents
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Abstract
The invention discloses a traffic signal lamp timing optimization method considering complex microenvironment characteristics, which fully considers the requirement of environmental protection, establishes a traffic signal lamp timing model considering both the improvement of traffic capacity and the reduction of tail gas pollution, takes the migration rule of the motor vehicle tail gas emission in the atmospheric environment into consideration, and highly pays attention to CO, HC and SO 2 、NO X The influence of the tail gas pollutants such as PM particles on the atmospheric pollution is obtained in a numerical simulation mode, the pollutant concentration of pedestrian height is brought into a target function of traffic signal lamp timing optimization, a traffic signal lamp timing multi-target model is built, and solution is realized; the multi-objective model provides a target for reducing the exposure of pollutants of traffic participants, and the multi-objective model has strong applicability in signal lamp timing by changing the weight value in the multi-objective model, so that the single-objective optimization requirement in traditional signal lamp timing can be met, and the multi-objective optimization requirement in signal lamp timing based on environmental protection can also be met.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a traffic signal lamp timing optimization method considering complex microenvironment characteristics.
Background
By integrating the current research situation at home and abroad, the following can be seen: experts and scholars at home and abroad bring traffic environmental pollution control factors into the category of traffic signal control and optimization, carry out a series of beneficial attempts at the macroscopic level of a road traffic network and the microscopic level of road sections and intersections, obtain fruitful results, and start a research enthusiasm in the field of traffic flow management and control, but the research on the following two aspects is slightly insufficient:
in the evaluation of the influence of a traffic signal lamp timing strategy on the tail gas emission of the motor vehicle, most researches are limited to the comparative analysis of the tail gas emission of the motor vehicle under the control of a signal lamp, the influence of specific signal control parameters (period, split green and the like) on the tail gas emission of the motor vehicle is not specified, and the contribution degree of the signal control parameters on the reduction of the tail gas emission of the motor vehicle is less considered.
When the reduction of the tail gas emission of the motor vehicle is taken as an important index for the timing optimization of the traffic signal lamp, so that the good effect of reducing the tail gas emission of the motor vehicle is achieved by most researches. However, in the optimization process, the transfer rule of the motor vehicle exhaust emission in the atmospheric environment is not considered, and the concentration contribution value of the traffic signal lamp timing method to urban atmospheric pollution and the influence degree on the exposure of pollutants of traffic participants (such as pedestrians) are less considered.
The traffic signal lamp timing is an important component of a traffic signal control system, and the reasonable signal lamp timing can effectively reduce traffic congestion and driving delay caused by mixed traffic and reduce tail gas emission of motor vehicles, thereby relieving increasingly severe road traffic congestion pressure and beautifying urban living environment. Although the signal lamp timing method in China has made great progress, the current signal lamp timing method still lags behind compared with the current situations of rapid expansion of urbanization, rapid increase of the number of motor vehicles, increasingly serious tail gas pollution and the like. The traditional signal lamp timing method takes the reduction of driving delay as a main target, and the urban road traffic system developed harmoniously in the future of 'human-vehicle-road-environment' requires that the signal lamp timing method also has the function of reducing tail gas pollution. Therefore, a signal lamp timing method comprehensively considering environmental factors and driving delay becomes a research focus in the field of traffic signal control, and becomes an important means for solving the problem of tail gas pollution of motor vehicles.
Disclosure of Invention
The invention aims to provide a traffic signal lamp timing optimization method considering complex microenvironment characteristics, so that a signal lamp timing multi-objective model has strong applicability, and not only can meet the single-objective optimization requirement of traditional signal lamp timing, but also can meet the signal lamp timing multi-objective optimization requirement based on environmental protection.
In order to realize the task, the invention adopts the following technical scheme:
a traffic signal timing optimization method considering complex microenvironment characteristics comprises the following steps:
calculating to obtain a VSP value by using a motor vehicle specific power formula, and further establishing a direct relation between the motor vehicle operation condition distribution and VSP and tail gas emission;
analyzing the influence degree and sensitivity of the motor vehicle running condition distribution and microenvironment characteristics on the motor vehicle exhaust pollutant emission by using a sensitivity analysis method, so as to deeply excavate main factors influencing the motor vehicle exhaust emission characteristics, construct a basic framework of a motor vehicle exhaust emission model, and locally correct the motor vehicle exhaust emission model;
establishing a three-dimensional turbulent flow non-compressible N-S equation for describing tail gas diffusion flow, discretizing a control equation by adopting a finite volume method, reproducing the motion characteristics of a motor vehicle flow by utilizing a computational fluid dynamics Fluent simulation tool, completing dynamic numerical simulation of motor vehicle tail gas pollutant diffusion to obtain the derivative change and diffusion rule of the motor vehicle tail gas pollutant under the combined action of dynamic environment parameters and random traffic flow parameters to obtain the migration rule of motor vehicle tail gas emission in the atmospheric environment, and then establishing a coupling relation between the motor vehicle tail gas emission and traffic flow characteristics according to the concentration value of pedestrian height or other height needing to be observed;
the method comprises the steps of establishing a multi-objective model of traffic signal light timing optimization by taking the average running delay of the motor vehicle as small as possible, the tail gas pollutant emission of the motor vehicle as small as possible and the pollutant exposure of traffic participants as low as possible as evaluation indexes, and solving to obtain a traffic signal light timing optimization result.
Further, the VSP value is calculated by utilizing a motor vehicle specific power formula, and then a direct relation between the motor vehicle operation condition distribution and the VSP and the exhaust emission is established, and the method comprises the following steps:
step 1.1, the motor vehicle ratio formula is as follows:
VSP＝v×[1.1a+9.81×(atan(sinG))+0.132]+0.000302v 3 (1)
in the above formula, VSP represents the specific power of the vehicle, KW/t; v represents velocity, m/s; a represents acceleration, m/s 2 (ii) a G represents a road gradient;
calculating the instantaneous VSP value of the acquired instantaneous speed and acceleration of the motor vehicle by using the formula;
step 1.2, establishing a direct relation between the operation condition of the motor vehicle and VSP and tail gas emission, wherein the specific formula is as follows:
in the above formula, E represents the total amount of exhaust gas emission, g, over a certain period of time; i represents a travel time (i ═ 1,2,3, …, m), s; p is a radical of i Represents the instantaneous VSP value at the moment of the ith second; f (p) i ) Representing the instantaneous VSP value p of the vehicle in the running time i The number of times the value is counted; r (p) i ) Representing the instantaneous VSP value at the moment of ith second as p i Average discharge rate of value, g/s;
if the measurement unit of the pollutant concentration data is ppm, the pollutant emission rate needs to be calculated according to the tail gas mass flow, and the specific implementation method comprises the following steps:
in the above formula, R (p) i ) Represents the emission rate of a certain pollutant, g/s; MF represents the mass flow of tail gas, kg/h; EC represents the concentration of a certain contaminant, ppm.
Further, the analysis of the influence degree and the sensitivity of the motor vehicle operation condition distribution and the microenvironment characteristics on the motor vehicle exhaust pollutant emission by using a sensitivity analysis method comprises the following steps:
step 2.1, according to the size of the sensitivity coefficient SAF, the sensitivity degree is divided into general sensitivity, sensitivity and insensitivity, and the specific formula is as follows:
in the above formula, Δ A/A represents the rate of change of the emission factor; Δ F/F represents the rate of change of the model parameters;
when the absolute SAF absolute is less than or equal to 0.1, the parameter is insensitive to the influence of the tail gas emission of the motor vehicle and belongs to an insensitive parameter; when the SAF is more than or equal to 1, the parameter is very sensitive to the influence of the tail gas emission of the motor vehicle and belongs to a sensitive parameter; when the SAF is 0.1< 1, the influence of a certain parameter on the tail gas emission of the motor vehicle is general, and the parameter belongs to general sensitive parameters.
Further, the deep excavation of the main factors affecting the exhaust emission characteristics of the motor vehicle, the construction of the basic architecture of the motor vehicle exhaust emission model, and the local correction of the motor vehicle exhaust emission model comprises the following steps:
step 2.2, collecting data according to the key model parameters of the researched target city, wherein the data comprise the geographic position and the climate condition of the target city, the age distribution of the motor vehicle, the fuel condition and the driving speed;
step 2.3, firstly, collecting concentration data of the motor vehicle tail gas pollutants through vehicle-mounted tail gas emission detection and a vehicle data recorder, and converting the data into motor vehicle tail gas emission; then, the running speed, the service life, the sulfur content in fuel oil, the temperature and the humidity of the vehicle are taken as influence parameters, a sensitive coefficient SAF calculation formula is utilized, and the MOVES model of the motor vehicle exhaust emission model is operated to calculate and obtain CO and CO by changing the value of a certain parameter and keeping the values of other parameters unchanged 2 、HC、NOx、SO 2 、PM 2.5 、PM 10 And (3) the pollutant emission is converted into SAF to carry out sensitivity analysis, and then the sensitivity of a plurality of influencing parameters to the emission of the pollutants in the tail gas of the motor vehicle is determined, so that the MOVES model of the tail gas emission model of the motor vehicle is corrected.
Further, the establishing of the three-dimensional turbulent flow incompressible N-S equation describing the diffusion flow of the exhaust gas comprises the following steps:
step 3.1, the control equation consists of a mass, momentum and energy conservation equation and a k-epsilon turbulence model, and the calculation formula of the mass conservation equation is as follows:
in the above formula, ρ represents a fluid concentration; x is a radical of a fluorine atom i Represents a component in the i direction; u. of i Represents the fluid velocity in the i direction, m/s;
for incompressible fluids, the fluid concentration ρ is constant and equation (5) can be simplified as:
the formula for the calculation of the conservation of momentum equation is:
in the above formula, τ ij Expressing the shear stress tensor, which is calculated by the formula (8); p represents static pressure, Pa; ρ g i Represents the volumetric force in the i direction; f i Representing source items caused by pollution sources; u. of j Represents the molecular viscosity in the j direction; x is the number of j The representation is a component in the j direction;
the formula for the energy conservation equation is:
in the above formula, k represents molecular thermal conductivity, W/m.K; k is a radical of t Represents the thermal conductivity caused by turbulent diffusion, calculated from equation (10); s h Representing the defined heat source volume; p r Represents a prandtl constant;
the k-epsilon turbulence model expressed by the turbulence kinetic energy k and the dissipation coefficient epsilon has the calculation formula as follows:
in the above formula, u denotes the ground friction speed; c u For empirical coefficients, K is typically set to 0.4.
Further, in the process of discretizing the control equation by adopting a finite volume method, when the numerical simulation analysis of the physical model is carried out, a mode of combining a structural grid and an unstructured grid is adopted to ensure the precision of a calculation result.
Further, the dynamic numerical simulation of the diffusion of pollutants in the tail gas of the motor vehicle is completed by reproducing the motion characteristics of the motor vehicle flow by using a computational fluid dynamics Fluent simulation tool, and comprises the following steps:
solving an N-S flow control equation by using a Fluent numerical simulation method in computational fluid mechanics, and setting boundary conditions of simulation according to meteorological conditions such as wind speed, temperature and humidity; the boundary conditions include an entry boundary condition and an exit boundary condition, wherein:
the inlet boundary condition can be divided into two parts, one part is the inlet boundary of wind, and the other part is the pollutant inlet boundary:
boundary of air inlet: the inlet wind profile may be described as:
u y ＝u ref (y/y ref ) α (13)
in the above formula, u y Represents the wind speed at height y, m/s; u. of ref Indicates the height y ref Wind speed, m/s; y is ref Representing the height of a reference point in the gas boundary layer; α represents a section index;
contaminant entrance boundary: obtaining the mass flow rate E/t of the tail gas pollutants with the unit of g/s as the inlet boundary of the pollutants by combining the total tail gas emission amount E of a certain period of time obtained by the calculation of the formula (2) with the acquisition time t of the concentration data of the tail gas pollutants;
exit boundary conditions:
the flow of fluid is considered to be fully developed at the outlet boundary, which is therefore considered to be a free-flow outlet.
Further, the multi-objective model for traffic signal timing optimization is established by taking the average running delay of the motor vehicle as small as possible, the emission of pollutants in the tail gas of the motor vehicle as small as possible and the exposure of pollutants of traffic participants as low as possible as evaluation indexes, and comprises the following steps:
step 4.1, reflecting the traffic signal lamp timing multi-objective optimization function of the three evaluation indexes, which can be expressed as:
min{w 1 A 1 +w 2 A 2 +w 3 A 3 } (14)
in the above formula, A 1 、A 2 、A 3 Respectively representing three targets of average driving delay, tail gas pollutant emission and pollutant exposure of traffic participants; w is a 1 、w 2 、w 3 Respectively represent the weights of three targets, the values of the weights are in the range of (0, 1), and omega is required 1 +ω 2 +ω 3 ＝1；
To eliminate the effects of the dimension, equation (14) can be expressed as:
in the above formula, D represents the average delay of the vehicle after the signal timing scheme is optimized, s; d 0 The average delay of the motor vehicle, s, representing the original signal timing scheme; e represents the motor vehicle exhaust pollutant emission g after the signal timing scheme is optimized; d and D 0 The delay time is calculated according to the measured data and the HCM2000 delay model; e 0 Representing the motor vehicle exhaust pollutant emission of the original signal timing scheme, g; e and E 0 Can be calculated by using the formula (2); r represents the pollutant exposure level of the traffic participants after the signal timing scheme is optimized, and mg/(kg.s); r 0 Represents the contaminant exposure level of the traffic participants, mg/(kg.s), R and R, of the original signal timing scheme 0 The calculation method of (a) is expressed as:
in the above formula, CA represents the concentration of the pollutant in the atmosphere, mg/m 3 (ii) a IR denotes respiration Rate, m 3 S; EF represents the exposure frequency, s/a; ED represents the duration of contaminant exposure, a; BW means body weight, kg; AT represents the mean exposure time of the contaminant, s;
and 4.2, expressing the constraint conditions of the traffic signal lamp timing multi-objective optimization function as follows:
C min ≤C≤C max (17)
g min ≤g i ≤g max (18)
equation (17) is a constraint condition of the signal period C, where C min The shortest cycle duration of the intersection is represented as s; c max Representing the longest period duration of the intersection, s; the green time g for each phase is given by the formula (18) i Constraint of (a) in which g min A minimum green time, s, representing the ith phase; g max The maximum green time, s, for the ith phase is indicated.
Further, the solving algorithm of the multi-target model adopts a self-adaptive particle swarm algorithm.
Compared with the prior art, the invention has the following technical characteristics:
1. the traffic signal lamp timing method provided by the invention fully considers the requirement of environmental protection, establishes a traffic signal lamp timing model which can improve the traffic capacity and reduce the tail gas pollution, and designs an effective method for traffic signal lamp timing optimization by utilizing an improved PSO algorithm, thereby completing the solution of a traffic signal lamp timing optimization multi-objective model. The main characteristics are as follows: compared with the existing research results at home and abroad, the traffic signal lamp timing scheme established by the invention takes the migration rule of the motor vehicle tail gas emission in the atmospheric environment into consideration, and highly pays attention to CO, HC and SO 2 、NO X And the influence of the tail gas pollutants such as PM particles on the atmospheric pollution is obtained in a numerical simulation mode, and then the pollutant concentration of pedestrian height is brought into a target function of traffic signal light timing optimization, a traffic signal light timing multi-target model is constructed, and solution is realized.
2. The traffic signal lamp timing scheme established by the invention fully considers the complex environmental characteristics of the intersection, realizes the overall consideration of traffic flow characteristics, environmental climate characteristics and pedestrian exposure characteristics, and researches the influence of specific signal control parameters (period, green-to-green ratio and the like) on the exhaust emission of the motor vehicle through the sensitivity analysis of traffic signal lamp timing optimization. The main characteristics are as follows: when a traffic signal lamp timing multi-target model is constructed, the goal of reducing the exposure of pollutants of traffic participants is provided, and the signal lamp timing multi-target model has strong applicability by changing the size of the weighted value in the multi-target model, so that the single-target optimization requirement of the traditional signal lamp timing can be met, and the signal lamp timing multi-target optimization requirement based on environmental protection can also be met.
Drawings
FIG. 1 is a schematic flow chart of the overall design, research and verification process of the scheme of the invention.
Detailed Description
Referring to the attached figure 1, the invention discloses a traffic signal lamp timing optimization method considering complex microenvironment characteristics, which comprises the following steps:
step 1, calculating by using a Vehicle Specific Power (VSP) formula to obtain a VSP value, and further establishing a direct relation between the vehicle operation condition distribution and VSP and exhaust emission. The method specifically comprises the following steps:
step 1.1, the motor vehicle ratio formula is as follows:
VSP＝v×[1.1a+9.81×(atan(sinG))+0.132]+0.000302v 3 (1)
at the upper partIn the middle, VSP represents the specific power of the motor vehicle, KW/t; v represents velocity, m/s; a represents acceleration, m/s 2 (ii) a G represents a road gradient.
And calculating the instantaneous VSP value by utilizing the formula for the acquired instantaneous speed and acceleration of the motor vehicle.
Step 1.2, establishing a direct relation between the operation condition of the motor vehicle and VSP and tail gas emission, wherein the concrete implementation formula is as follows:
in the above formula, E represents the total amount of exhaust gas emission, g, over a certain period of time; i denotes the journey time (i ═ 1,2,3, …, m), s; p is a radical of formula i Represents the instantaneous VSP value at the moment of the ith second; f (p) i ) Representing the instantaneous VSP value p of the vehicle in the running time i The number of times the value is counted; r (p) i ) Representing the instantaneous VSP value at the moment of ith second as p i Average discharge rate of value, g/s.
In this step, it is particularly noted that, in the detection data of the pollutant emission in the tail gas of the motor vehicle, if the measurement unit of the pollutant concentration data is Parts per million (ppm), the pollutant emission rate needs to be calculated according to the mass flow of the tail gas, and the specific implementation method is as follows:
in the above formula, R (p) i ) Represents the emission rate of a certain pollutant, g/s; MF represents the mass flow of tail gas, kg/h; EC represents the concentration of a certain contaminant, ppm.
And 2, analyzing the influence degree and sensitivity of micro-environmental characteristics such as the running condition distribution, the temperature and the humidity of the motor vehicle on the motor vehicle exhaust pollutant emission by using a sensitivity analysis method, so as to deeply excavate main factors influencing the motor vehicle exhaust emission characteristic, construct a basic framework of a motor vehicle exhaust emission model, and locally correct the motor vehicle exhaust emission model. The method comprises the following steps:
step 2.1, according to the size of a sensitivity coefficient (SAF), the sensitivity degree is divided into general sensitivity, sensitivity and insensitivity, and a specific formula is as follows:
in the above formula, Δ A/A represents the rate of change of the emission factor; Δ F/F represents the rate of change of the model parameters.
When the absolute SAF absolute is less than or equal to 0.1, the parameter is insensitive to the influence of the tail gas emission of the motor vehicle and belongs to an insensitive parameter; when the SAF is more than or equal to 1, the parameter is very sensitive to the influence of the tail gas emission of the motor vehicle and belongs to a sensitive parameter; when the SAF is 0.1< 1, the influence of a certain parameter on the tail gas emission of the motor vehicle is general, and the parameter belongs to general sensitive parameters.
And 2.2, in order to analyze the influence of various influence parameters on the tail gas emission characteristics of the motor vehicle so as to complete the local correction of the MOVES model for predicting the tail gas emission of the motor vehicle, data collection is required according to key model parameters of a researched target city, wherein the data collection comprises the geographic position and the weather condition of the target city, the age distribution, the fuel condition, the driving speed and the like of the motor vehicles such as private cars, gasoline cars, diesel cars and the like.
Step 2.3, selecting Guangzhou as a target city, firstly, collecting concentration data of the motor vehicle tail gas pollutants through a vehicle-mounted tail gas emission detector (PEMS) and a vehicle data recorder (GPS), and converting the concentration data into the motor vehicle tail gas emission according to the step 1; then, the driving speed, the age, the sulfur content in the fuel oil, the temperature and the humidity of the vehicle are taken as influence parameters, and the value of one parameter is changed by using the formula in the step 2.1, and the values of other parameters are kept unchanged, so that the MOVES model of the motor vehicle exhaust emission is operated to calculate and obtain CO and CO 2 、HC、NOx、SO 2 、PM 2.5 、PM 10 Waiting for the emission of pollutants, converting the emission into SAF (safety and safety valve) for sensitivity analysis, and further determining the sensitivity of five influence parameters to the emission of the pollutants in the tail gas of the motor vehicle as follows: speed of travel and vehicleThe influence of the age on the emission of pollutants in the tail gas of a motor vehicle is large, and the method belongs to sensitive parameters; the sulfur content and temperature of the fuel oil have less obvious influence on the pollutant emission of the tail gas of the motor vehicle and belong to common sensitive parameters; the influence of humidity on the emission of pollutants in the tail gas of a motor vehicle is small, and the humidity is an insensitive parameter. In the step, through sensitivity analysis, the driving speed and the vehicle age are obtained to be important factors influencing the emission of the tail gas pollutants of the motor vehicle, and the two influencing factors need to be concerned when signal lamp timing is optimized, particularly the driving speed needs to be concerned; in addition, after the measured data is compared with the corrected MOVES model simulation data, the emission factors obtained by the measured data and the corrected MOVES model simulation data are basically consistent, and the localized correction of the MOVES model is considered, so that a theoretical basis is provided for the subsequent emission factor calculation.
And 3, establishing a three-dimensional turbulence flow non-compressible N-S equation for describing tail gas diffusion flow, discretizing a control equation by adopting a finite volume method, reproducing the motion characteristics of the motor vehicle flow by utilizing a computational fluid dynamics Fluent simulation tool, finishing dynamic numerical simulation of motor vehicle tail gas pollutant diffusion, obtaining the derivative change and diffusion rule of the motor vehicle tail gas pollutant under the combined action of dynamic environment parameters such as wind speed, temperature and humidity and random traffic flow parameters such as flow and speed, obtaining the migration rule of motor vehicle tail gas emission in the atmospheric environment, and then establishing the coupling relation between the motor vehicle tail gas emission and the traffic flow characteristics according to the pedestrian height or other concentration values needing to be observed in height. The method comprises the following steps:
and 3.1, forming a control equation by a mass, momentum and energy conservation equation and a k-epsilon turbulence model. The formula for the mass conservation equation is:
in the above formula, ρ represents a fluid concentration; x is the number of i Represents a component in the i direction; u. of i Representing the fluid velocity in the i direction, m/s.
For incompressible fluids, the fluid concentration ρ is constant and equation (5) can be simplified as:
the formula for the calculation of the conservation of momentum equation is:
in the above formula, τ ij Expressing the shear stress tensor, which is calculated by the formula (8); p represents static pressure, Pa; ρ g i Represents the volumetric force in the i direction; f i Representing source items caused by pollution sources; u. of j Represents the molecular viscosity in the j direction; x is the number of j The representation is a component in the j direction.
The formula for the energy conservation equation is:
in the above formula, k represents molecular thermal conductivity, W/m.K; k is a radical of t Represents the thermal conductivity caused by turbulent diffusion, calculated from equation (10); s h Representing the defined heat source volume; p r Representing the prandtl constant.
The k-epsilon turbulence model expressed by the turbulence kinetic energy k and the dissipation coefficient epsilon has the calculation formula as follows:
in the above formula, u * The ground friction speed is represented and is 0.13; c u A typical value is 0.09 for empirical coefficients; k is typically set to 0.4.
And 3.2, carrying out discretization processing on the control equation by using a finite volume method, wherein the influence of the microenvironment where the intersection is located, traffic participants, ground facilities and the like is considered, and when carrying out numerical simulation analysis on the physical model, a mode of combining a structural grid and a non-structural grid is adopted to ensure the precision of a calculation result.
And 3.3, solving the N-S flow control equation by using a Fluent numerical simulation method in Computational Fluid Dynamics (CFD), and setting boundary conditions of simulation according to meteorological conditions such as wind speed, temperature, humidity and the like. The boundary conditions are set because numerical simulation of the diffusion of contaminants is generally performed in a limited space, and for this reason, the boundary conditions need to be specified for a given limited space. The boundary conditions generally relate to two types of boundary conditions: an entry boundary condition and an exit boundary condition.
(1) Entry boundary condition
The inlet boundary condition may be divided into two parts, one part being the wind inlet boundary and the other part being the contaminant inlet boundary.
1) Boundary of air inlet
The inlet wind profile may be described as:
u y ＝u ref (y/y ref ) α (13)
in the above formula, u y Represents the wind speed at height y, m/s; u. of ref Indicates the height y ref Wind speed, m/s; y is ref Representing the height of a reference point in the gas boundary layer; α represents a section index, and is generally 0.23.
2) Boundary of pollutant entrance
And (3) combining the E obtained by the calculation of the formula (2) with the acquisition time t of the concentration data of the tail gas pollutants to obtain the mass flow E/t of the tail gas pollutants, wherein the unit is g/s and the mass flow E/t is used as the boundary of the pollutant inlet.
(2) Outlet boundary condition
The flow of fluid is considered to be fully developed at the outlet boundary, which is therefore considered to be a free-flow outlet.
And 3.4, obtaining the change of a wind field and the migration rule of the tail gas emission of the motor vehicle in the atmospheric environment by using the simulation result of Fluent. In this step, according to the position of the intersection and the surrounding buildings, it is assumed that the intersection is respectively located in a common street canyon (the aspect ratio H/W of the street canyon is 1:1), an ideal street canyon (the aspect ratio H/W of the street canyon is 1:2) and a deep street canyon (the aspect ratio H/W of the street canyon is 2:1), and the migration characteristics of the exhaust pollutants under the same meteorological conditions are studied.
Step 3.5, according to the simulation result and the migration rule in the step 3.4, a concentration value at the height (1.5m) of a pedestrian or other positions needing to be observed is searched, and the coupling relation between the tail gas emission of the motor vehicle and the traffic flow characteristics is deeply excavated, for example, when the pollutant emission is excavated when the traffic constitutes different, the following can be found:
(1) traffic structure to CO, HC and NO X The diffusion of (a) has a significant impact, including the traffic structure of a smaller number of cars and a larger number of buses, which results in a reduction in both the amount and concentration of exhaust emissions.
(2) Wind speed variation vs. CO, HC and NO X The diffusion of (a) is important, as the wind speed increases, there is a significant drop in the pollutant concentration on both the leeward and windward sides.
(3) At pedestrian height (1.5m), the leeward side CO, HC and NO were measured X The average concentration is 2 times the upwind side, for which reason the traffic participant contaminant exposure in step 4 should be calculated as far as possible using the concentration data on the lee side.
And 4, establishing a multi-objective model of traffic signal lamp timing optimization by taking the 'average running delay of the motor vehicle is as small as possible, the tail gas pollutant emission of the motor vehicle is as small as possible, and the pollutant exposure of traffic participants is as low as possible' as evaluation indexes, and designing a solving algorithm of the model to obtain a traffic signal lamp timing optimization result.
Step 4.1, reflecting the traffic signal lamp timing multi-objective optimization function of the three evaluation indexes, can be expressed as:
min{w 1 A 1 +w 2 A 2 +w 3 A 3 } (14)
in the above formula, A 1 、A 2 、A 3 Respectively representing three targets of average driving delay, tail gas pollutant emission and pollutant exposure of traffic participants; w is a 1 、w 2 、w 3 Respectively represent the weights of three targets, the values of the weights are in the range of (0, 1), and omega is required 1 +ω 2 +ω 3 ＝1。w 1 、w 2 、w 3 Expresses the degree of importance, w, of the decision maker to the three targets 1 The larger the value is, the more important the decision maker pays attention to the average driving delay, the more inclined the decision maker is to optimize the single objective function in the traditional signal lamp timing, on the contrary, the decision maker pays attention to the influence of the tail gas of the motor vehicle on the atmospheric environment and the health of pedestrians while paying attention to the average driving delay, and the more inclined the decision maker is to optimize the multiple objectives in the signal lamp timing based on the environmental protection.
To eliminate the effects of the dimension, equation (14) can be expressed as:
in the above formula, D represents the average delay of the vehicle after the signal timing scheme is optimized, s; d 0 The average delay of the motor vehicle, s, representing the original signal timing scheme; e represents the motor vehicle exhaust pollutant emission g after the signal timing scheme is optimized; d and D 0 The delay time is calculated according to the measured data and the HCM2000 delay model; e 0 Representing the motor vehicle exhaust pollutant emission of the original signal timing scheme, g; e and E 0 Can be calculated by using the formula (2); r represents the pollutant exposure level of the traffic participants after the signal timing scheme is optimized, and mg/(kg.s); r 0 Traffic parameters representing original signal timing schemeContaminant exposure level, mg/(kg.s), R and R 0 The calculation method of (a) is expressed as:
in the above formula, CA represents the concentration of the pollutant in the atmosphere, mg/m 3 (ii) a IR denotes respiration Rate, m 3 S; EF represents the exposure frequency, s/a; ED represents the duration of contaminant exposure, a; BW means body weight, kg; AT represents the mean exposure time of the contaminant, s.
And 4.2, expressing the constraint conditions of the traffic signal lamp timing multi-objective optimization function as follows:
C min ≤C≤C max (17)
g min ≤g i ≤g max (18)
equation (17) is a constraint condition of the signal period C, where C min The shortest cycle duration of the intersection is represented as s; c max Representing the longest cycle length at the intersection, s. The green time g for each phase is given by the formula (18) i Constraint of (a) in which g min A minimum green time, s, representing the ith phase; g max Represents the maximum green time, s, for the ith phase.
Step 4.3, the actual requirements of the multi-objective model are optimized in the time distribution of the traffic signal, the optimal solution of the formula (15) is found by using an Adaptive Particle Swarm Optimization (APSO), and the specific method is as follows:
(1) the position and velocity of the particle population are randomly initialized.
(2) The fitness of each particle is calculated.
(3) The fitness value of each particle is compared to the historical best position (Pbest) it has experienced i ) And if the fitness value is better, the fitness value is taken as the optimal position of the individual.
(4) The fitness value of each particle is compared with the global history best position (Gbest) i ) If it is better, it is taken asA global optimum location.
(5) The velocity and position of the particle are updated to create a new particle.
(6) And introducing a simple mutation operator, and reinitializing the particles with a certain probability after each particle update.
(7) And if the termination condition is not reached, returning to the step (2) in the step.
Step 5, Effect evaluation
According to an actual intersection prototype, collected and actual traffic parameters and signal lamp timing data are used as important parameters of simulation and input into a traffic simulation platform, so that comparative analysis of pollutant emission amount before and after signal lamp timing multi-objective optimization, driving delay and the like is performed, the effect of the intersection signal lamp timing multi-objective optimization is explained, and the effect evaluation of the intersection signal lamp timing optimization is realized.
And 5.1, collecting road condition and traffic condition data, including the number of each entrance lane at the intersection, traffic volume, the current signal timing scheme, saturation, pollutant concentration, temperature, humidity and other meteorological condition data.
Step 5.2, according to the previous steps, calculating to obtain the optimized signal timing parameters, completing the multi-objective function construction of the formula (15), and setting the numerical value of each item weight, such as omega 1 ＝0.2，ω 2 ＝ω 3 ＝0.4。
And 5.3, setting the number of the initial population as 100, taking the maximum iteration number as 1000, carrying out multiple experiments, obtaining a fitness value after the operation of each experiment is finished, and taking the operation result with the maximum fitness value as the optimal scheme for traffic signal timing.
And 5.4, respectively inputting signal timing schemes before and after optimization into traffic simulation software (such as Paramics and VISSIM), and comparing simulation results to know the signal timing optimization control effect of the urban road intersection and the optimization effect of reducing delay and tail gas emission.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (7)
1. A traffic signal timing optimization method considering complex microenvironment characteristics is characterized by comprising the following steps:
calculating to obtain a VSP value by using a motor vehicle specific power formula, and further establishing a direct relation between the motor vehicle operation condition distribution and VSP and tail gas emission;
analyzing the influence degree and sensitivity of the motor vehicle running condition distribution and microenvironment characteristics on the motor vehicle exhaust pollutant emission by using a sensitivity analysis method, so as to deeply excavate main factors influencing the motor vehicle exhaust emission characteristics, construct a basic framework of a motor vehicle exhaust emission model, and locally correct the motor vehicle exhaust emission model;
establishing a three-dimensional turbulent flow non-compressible N-S equation for describing tail gas diffusion flow, discretizing a control equation by adopting a finite volume method, reproducing the motion characteristics of a motor vehicle flow by utilizing a computational fluid dynamics Fluent simulation tool, completing dynamic numerical simulation of motor vehicle tail gas pollutant diffusion to obtain the derivative change and diffusion rule of the motor vehicle tail gas pollutant under the combined action of dynamic environment parameters and random traffic flow parameters to obtain the migration rule of motor vehicle tail gas emission in the atmospheric environment, and then establishing a coupling relation between the motor vehicle tail gas emission and traffic flow characteristics according to the concentration value of pedestrian height or other height needing to be observed;
the dynamic numerical simulation of the diffusion of the pollutants in the tail gas of the motor vehicle is completed by utilizing a computational fluid dynamics Fluent simulation tool to reproduce the motion characteristics of the motor vehicle flow, and comprises the following steps:
solving an N-S flow control equation by using a Fluent numerical simulation method in computational fluid mechanics, and setting boundary conditions of simulation according to meteorological conditions such as wind speed, temperature and humidity; the boundary conditions include an entry boundary condition and an exit boundary condition, wherein:
the inlet boundary condition can be divided into two parts, one part is the inlet boundary of wind, and the other part is the pollutant inlet boundary:
boundary of air inlet: the inlet wind profile may be described as:
u y ＝u ref (y/y ref ) α (13)
in the above formula, u y Represents the wind speed at height y, m/s; u. of ref Indicates the height y ref Wind speed, m/s; y is ref Representing the height of a reference point in the gas boundary layer; α represents a section index;
contaminant entrance boundary: obtaining the mass flow rate E/t of the tail gas pollutants with the unit of g/s as the inlet boundary of the pollutants by combining the total tail gas emission amount E of a certain period of time obtained by the calculation of the formula (2) with the acquisition time t of the concentration data of the tail gas pollutants;
exit boundary conditions:
the flow of fluid is considered to be fully developed at the outlet boundary, which is therefore considered to be a free-flow outlet;
establishing a multi-target model of traffic signal lamp timing optimization by taking the average running delay of the motor vehicle as small as possible, the tail gas pollutant emission of the motor vehicle as small as possible and the pollutant exposure of traffic participants as low as possible as evaluation indexes, and solving to obtain a traffic signal lamp timing optimization result;
the method for establishing the multi-objective model of traffic signal lamp timing optimization by taking the average running delay of the motor vehicle as small as possible, the tail gas pollutant emission of the motor vehicle as small as possible and the pollutant exposure of traffic participants as low as possible as evaluation indexes comprises the following steps:
step 4.1, reflecting the traffic signal lamp timing multi-objective optimization function of the three evaluation indexes, which can be expressed as:
min{w 1 A 1 +w 2 A 2 +w 3 A 3 } (14)
in the above formula, A 1 、A 2 、A 3 Respectively representing three targets of average driving delay, tail gas pollutant emission and pollutant exposure of traffic participants; w is a 1 、w 2 、w 3 Respectively represent the weights of three targets, the values of the weights are in the range of (0, 1), and omega is required 1 +ω 2 +ω 3 ＝1；
To eliminate the effects of the dimension, equation (14) can be expressed as:
in the above formula, D represents the average delay of the vehicle after the signal timing scheme is optimized, s; d 0 The average delay of the motor vehicle, s, representing the original signal timing scheme; e represents the motor vehicle exhaust pollutant emission g after the signal timing scheme is optimized; d and D 0 The delay time is calculated according to the measured data and the HCM2000 delay model; e 0 Representing the motor vehicle exhaust pollutant emission of the original signal timing scheme, g; e and E 0 Can be calculated by using the formula (2); r represents the pollutant exposure level of the traffic participants after the signal timing scheme is optimized, and mg/(kg.s); r 0 Represents the level of contaminant exposure of the traffic participants in the original signal timing scheme, mg/(kg.s), R and R 0 The calculation method of (a) is expressed as:
in the above formula, CA represents the concentration of the pollutant in the atmosphere, mg/m 3 (ii) a IR denotes respiration Rate, m 3 S; EF represents the exposure frequency, s/a; ED represents the duration of contaminant exposure, a; BW means body weight, kg; AT represents the mean exposure time of the contaminant, s;
and 4.2, expressing the constraint conditions of the traffic signal lamp timing multi-objective optimization function as follows:
C min ≤C≤C max (17)
g min ≤g i ≤g max (18)
formula (17) is a constraint condition of the signal period C, wherein C min The shortest cycle duration of the intersection is represented as s; c max Representing the longest period duration of the intersection, s; the green time g for each phase is given by the formula (18) i Constraint of (a) in which g min A minimum green time, s, representing the ith phase; g max Represents the maximum green time, s, for the ith phase.
2. The traffic signal lamp timing optimization method considering complex microenvironment characteristics according to claim 1, wherein the VSP value is calculated by using a vehicle specific power formula, and then a direct relation between vehicle operation condition distribution and VSP and exhaust emission is established, comprising:
step 1.1, the motor vehicle ratio formula is as follows:
VSP＝v×[1.1a+9.81×(atan(sinG))+0.132]+0.000302v 3 (1)
in the above formula, VSP represents the specific power of the vehicle, KW/t; v represents velocity, m/s; a represents acceleration, m/s 2 (ii) a G represents a road gradient;
calculating the instantaneous VSP value of the acquired instantaneous speed and acceleration of the motor vehicle by using the formula;
step 1.2, establishing a direct relation between the operation condition of the motor vehicle and VSP and tail gas emission, wherein the specific formula is as follows:
in the above formula, E represents the total amount of exhaust gas emission, g, over a certain period of time; i represents a travel time (i ═ 1,2,3, …, m), s; p is a radical of i Represents the instantaneous VSP value at the moment of the ith second; f (p) i ) Representing the instantaneous VSP value p of the vehicle in the running time i Order of valueCounting; r (p) i ) Representing the instantaneous VSP value at the moment of ith second as p i Average discharge rate of value, g/s;
if the measurement unit of the pollutant concentration data is ppm, the pollutant emission rate needs to be calculated according to the tail gas mass flow, and the specific implementation method comprises the following steps:
in the above formula, R (p) i ) Represents the emission rate of a certain pollutant, g/s; MF represents the mass flow of tail gas, kg/h; EC represents the concentration of a certain contaminant, ppm.
3. The traffic signal lamp timing optimization method considering complex microenvironment characteristics according to claim 1, wherein the analysis of the influence degree and sensitivity of the motor vehicle operation condition distribution and the microenvironment characteristics on the motor vehicle exhaust pollutant emission by using a sensitivity analysis method comprises:
step 2.1, according to the size of the sensitivity coefficient SAF, the sensitivity degree is divided into general sensitivity, sensitivity and insensitivity, and the specific formula is as follows:
in the above formula, Δ A/A represents the rate of change of the emission factor; Δ F/F represents the rate of change of the model parameters;
when the absolute SAF absolute is less than or equal to 0.1, the parameter is insensitive to the influence of the tail gas emission of the motor vehicle and belongs to an insensitive parameter; when the SAF is more than or equal to 1, the parameter is very sensitive to the influence of the tail gas emission of the motor vehicle and belongs to a sensitive parameter; when the SAF is 0.1< 1, the influence of a certain parameter on the tail gas emission of the motor vehicle is general, and the parameter belongs to general sensitive parameters.
4. The traffic signal lamp timing optimization method considering complex microenvironment characteristics according to claim 1, wherein the deep excavation of main factors affecting the motor vehicle exhaust emission characteristics, the construction of a basic architecture of a motor vehicle exhaust emission model, and the local modification of the motor vehicle exhaust emission model comprises:
step 2.2, collecting data according to the key model parameters of the researched target city, wherein the data comprise the geographic position and the climate condition of the target city, the age distribution of the motor vehicle, the fuel condition and the driving speed;
step 2.3, firstly, collecting concentration data of the motor vehicle tail gas pollutants through vehicle-mounted tail gas emission detection and a vehicle data recorder, and converting the data into motor vehicle tail gas emission; then, the running speed, the service life, the sulfur content in fuel oil, the temperature and the humidity of the vehicle are taken as influence parameters, a sensitive coefficient SAF calculation formula is utilized, and the MOVES model of the motor vehicle exhaust emission model is operated to calculate and obtain CO and CO by changing the value of a certain parameter and keeping the values of other parameters unchanged 2 、HC、NOx、SO 2 PM2.5 and PM10 pollutants, and converting the emission into SAF for sensitivity analysis, and further determining the sensitivity of a plurality of influence parameters to the emission of the automobile exhaust pollutants so as to correct the MOVES model of the automobile exhaust emission model.
5. The traffic signal timing optimization method considering complex microenvironment characteristics according to claim 1, wherein the establishing of the three-dimensional turbulent flow non-compressible N-S equation describing the exhaust diffusion flow comprises:
step 3.1, the control equation consists of a mass, momentum and energy conservation equation and a k-epsilon turbulence model, and the calculation formula of the mass conservation equation is as follows:
in the above formula, ρ represents a fluid concentration; x is the number of i Represents the component in the i direction; u. of i Represents the fluid velocity in the i direction, m/s;
for incompressible fluids, the fluid concentration ρ is constant and equation (5) can be simplified as:
the formula for the calculation of the conservation of momentum equation is:
in the above formula, τ ij Expressing the shear stress tensor, which is calculated by the formula (8); p represents static pressure, Pa; ρ g i Represents the volumetric force in the i direction; f i Representing source items caused by pollution sources; u. of j Represents the molecular viscosity in the j direction; x is the number of j The representation is a component in the j direction;
the formula for the energy conservation equation is:
in the above formula, k represents molecular thermal conductivity, W/m.K; k is a radical of t Represents the thermal conductivity caused by turbulent diffusion, calculated from equation (10); s h Representing the defined heat source volume; p r Represents a prandtl constant;
the k-epsilon turbulence model expressed by the turbulence kinetic energy k and the dissipation coefficient epsilon has the calculation formula as follows:
in the above formula, u * Representing ground friction speed; c u For empirical coefficients, K is typically set to 0.4.
6. The traffic signal lamp timing optimization method considering complex microenvironment characteristics according to claim 1, wherein in the process of discretizing the control equation by using a finite volume method, a mode of combining a structural grid and a non-structural grid is adopted to ensure the accuracy of the calculation result when performing numerical simulation analysis of a physical model.
7. The traffic signal lamp timing optimization method considering complex microenvironment characteristics according to claim 1, wherein the solution algorithm of the multi-objective model adopts a self-adaptive particle swarm algorithm.
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