CN116434575B - Bus green wave scheme robust generation method considering travel time uncertainty - Google Patents
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Abstract
The invention discloses a robust generation method of a public traffic green wave scheme considering travel time uncertainty, which relates to the technical field of urban public transportation, and comprises the steps of firstly acquiring traffic data related to public traffic position and identity identification data, social vehicle traffic flow data, urban road network static data and the like, preprocessing the data, acquiring vehicle position and speed information according to the traffic data, and quantifying the travel time uncertainty of a road section bus; secondly, calculating the initial queuing length and the initial queuing dissipation time of the social vehicle so as to construct constraint conditions; finally, the uncertainty distribution of the travel time of the bus is input, a classical bus green wave optimization model MAXBOAND is improved, the uncertainty model is converted into a linear deterministic model through a stochastic optimization means, and then a genetic algorithm based on Monte Carlo simulation is adopted to solve the model.
Description
Technical Field
The invention relates to the technical field of urban public transportation, in particular to a robust generation method of a public transportation green wave scheme considering travel time uncertainty.
Background
The conventional bus operation management process comprises two means of time priority and space priority, wherein the space priority relates to the construction of a bus special lane, but the main urban areas of many cities in China do not have the condition of setting the bus special lane, and the bus mixed lane is still the most used mode at present; the time priority refers to bus signal priority, including offline bus signal priority, inductive bus signal priority and adaptive bus signal priority, the detection equipment and communication equipment required by the inductive bus signal priority and the adaptive bus signal priority are high in quality, investment and maintenance cost are high, and both strategies need good background signal timing schemes as bottom layer support, so that signal switching is frequent and tends to be more beneficial.
The existing main road green wave coordination control design method mainly comprises a graphic method, a numerical method, a model method and the like, the graphic method is simple, visual and clear to operate, but the optimized control variable type is limited, and the optimization process needs a designer to combine own experience, traverse the combination of various signal period durations and phase differences to obtain the maximum green wave bandwidth, so that the optimization process consumes time; the method has the advantages of strong operability, good replicability and small calculation amount, but needs to consider the difference of two-way traffic conditions and the phase sequence setting mode of intersections, and meanwhile, the difference between the optimal intersection spacing and the actual intersection spacing can also influence the effectiveness of the method.
The model method is a mathematical model for establishing the relationship between green wave bandwidth and parameters such as phase difference, signal period duration, vehicle running speed, phase sequence and the like, MAXBOAND is a classical model proposed in 1966, an objective function is that the sum of two-way green wave bandwidths of a trunk is maximum, decision variables of the model are signal period duration, green light duration, expected running speed of a vehicle, road section length and the like, and the related timing parameter analysis relationship is taken as a constraint condition, so that the parameters such as the phase difference, the green wave speed and the like of each intersection are solved, and the model method is a Mixed Integer Linear Programming (MILP) model.
In summary, it is needed to design a bus green wave scheme taking into account uncertainty of bus travel time and interaction of buses and social vehicles by constructing a related arterial road green wave coordination control model, and the existing bus green wave construction method still has the following disadvantages:
1. the existing bus green wave control construction method is carried out on the basis of setting a bus special lane, most of adopted bus speed design schemes are average, the bus running speed and the uncertainty of boarding and disembarking time of a platform are not considered, and the interaction influence of the bus and social vehicle mixing is not fully considered;
2. the existing main road green wave coordination control design adopts a graphic method and a mathematical method, has larger limitation, and the adopted model method has large calculation difficulty or poor actual application effect;
3. most of the existing public transportation bidirectional green wave control construction methods are deterministic solving problems, and the public transportation green wave robust optimization solving method does not consider the uncertainty of input variables.
Disclosure of Invention
In order to solve the technical problems, the invention provides a bus green wave scheme robust generation method considering travel time uncertainty, which comprises the following steps of
S1, acquiring traffic data, wherein the traffic data comprises bus position and identity identification data, social vehicle traffic flow data and urban road network static data, preprocessing the original data, analyzing the distribution characteristics of the bus data in different road sections, acquiring bus flow information and travel time information of buses in road sections, and quantifying the uncertainty of the bus travel time in the road sections in a probability distribution mode;
s2, considering traffic flow driving-away flow rates of traffic light states with different coordination phases of an upstream intersection, calculating initial queuing length of social vehicles during red light of the coordination phases of the downstream intersection according to the starting time difference of traffic light states of the upstream and downstream coordination phases, and calculating initial queuing dissipation time according to the traffic flow driving-away flow rates of the downstream intersection;
s3, taking the downstream intersection coordination phase and the red light queuing dissipation time into consideration, inputting the bus travel time uncertainty distribution, improving a classical bus green wave optimization model MAXBOAND, converting the uncertainty model into a deterministic model through a random optimization means, and then solving the model.
The technical scheme of the invention is as follows:
further, the step S1 specifically comprises the following sub-steps
S1.1, collecting relevant traffic data, wherein the traffic data comprise public transportation position and identity recognition data, social vehicle traffic flow data and urban road network static data;
s1.2, preprocessing bus position and identity identification data, carrying out map matching on longitude and latitude information and road network static information recorded by each data, extracting bus track points of each road section of a target trunk line, and obtaining the distribution condition of bus tracks;
s1.3, dividing low-frequency track data of the bus at time intervals of 1 hour, selecting a peak period with more track points for bus travel time uncertainty analysis, and finally quantifying the bus travel time uncertainty in the form of road section bus travel time probability distribution.
In the foregoing method for generating the bus green wave scheme robust by considering the uncertainty of the travel time, in step S1.1, the bus position and identity recognition data includes bus low-frequency track data, RFID radio frequency recognition data and video bus perception data;
social vehicle traffic flow data representing vehicle license plate data identified through video to provide passing information, wherein data fields of the passing information comprise equipment numbers, dates, detection time, license plate numbers, vehicle types, license plate colors, entrance lane numbers and lane numbers;
the urban road network static data includes vehicle detector device location data and signal control intersection related data.
In the foregoing method for generating public traffic green wave scheme robustness by considering travel time uncertainty, in step S2, the social vehicle initial queuing length during the downstream intersection coordination phase red light is calculated, including the following steps
S2.1.1 assuming n signal control intersections on the main road, the number of the signal control intersections is denoted by i, i=1, 2,3, …, n, S i And S is i+1 Two signal control intersections with the numbers of i and i+1 are respectively represented, C represents the common period duration of signal coordination, and r i And r i+1 Respectively represent intersections S i And S is i+1 When the red light of straight phaseLong g i And g i+1 Respectively represent intersections S i And S is i+1 Green light time length of straight phase; setting the vehicle to cross S i Travel to intersection S i+1 Is the upward direction from the intersection S i+1 Travel to intersection S i The direction of (2) is the downlink direction;
s2.1.2, assuming that the vehicle arrives uniformly and there is no fleet dispersion, calculate the vehicle arrival flow rate at the downstream intersection, intersection i+1:
the upstream intersection, i.e. intersection i coordinates the phase to red:
the coordination phase of the upstream intersection, namely intersection i, is green light:
wherein,and->Each lane arrival flow rate in Veh/Sec/Ln. for an upstream intersection, i.e., intersection i+1, to a downstream intersection, i.e., intersection i+1, during the red and green lights phase coordinated, respectively; />And->The vehicle flow reaching the downstream intersection, namely intersection i+1, respectively represents the vehicle flow reaching the downstream intersection, namely intersection i, in the period of the red light and the green light of the coordination phase of intersection i, and the unit is Veh;
representing the proportion of straight going to the downstream intersection of the traffic flow; r is (r) i Red light time representing uncoordinated phase in s; c represents a trunk coordination common period, and the unit is s; l (L) i+1 Representing the number of straight lanes of an entrance lane of a downstream intersection;
s2.1.3 simplifying the flow rate of vehicles exiting an upstream intersection into a piecewise function q i (t) two periods [0,2C ]]The vehicle exit flow rate conditions within can be expressed as:
s2.1.4, calculating a green light starting time difference of an upstream and downstream intersection:
wherein,the green light starting time difference of the upstream and downstream intersections is represented; />Indicating the phase difference of the upstream and downstream intersections, wherein when the value of the phase difference is more than or equal to 0, the phase start time of the downstream red light is not later than that of the upstream intersections;
s2.1.5 taking into account the different arrival flow rates of vehicles at the upstream junction to the downstream junction and the phase difference between the upstream and downstream junctionsAnd the time difference of the green light start time +.>The headway of the vehicle is uniformly set to be h, and four different scenes are listed:
case one:
and a second case:
case three:
case four:
calculating and summarizing the initial queuing length of vehicles at the downstream intersection:
wherein,representing the initial queue length of the vehicles at the downstream intersection.
In step S2, it is assumed that the vehicles are uniformly released and uniformly distributed on the downstream lane in the red light and green light periods of the trunk road straight-going phase, and the saturation flow rate of each lane in the trunk road straight-going phase green light period of the downstream intersection is recorded asThe vehicle arrival and departure all adopt the idea of point queuing, and calculate the initial queuing dissipation time, comprising the following steps of
S2.2.1, calculating the vehicle flow rate out of the upstream intersection, i.e. intersection i, in two cycles [0,2C ],
wherein q i (t) represents the upstream intersection, i.e. intersection i in two cycles [0,2C ]]The vehicle flow rate of the inner drive-out;
s2.2.2, representing and calculating the number of vehicles queued at downstream intersectionsThe number of vehicles arriving at the downstream intersection, i.e. intersection i +1, is equal to the number of vehicles exiting at different flow rates at the upstream intersection, i.e. intersection i, for a corresponding period of time,
wherein,the travel time of a road section when the coordination phase of a downstream intersection is red light is represented, and the unit is s;
calculating the initial queuing dissipation time of the downstream intersection, namely intersection i+1:
wherein τ i+1 The initial queuing dissipation time for the downstream intersection, intersection i+1, is shown.
The foregoing method for generating public traffic green wave scheme robustness by considering travel time uncertainty, wherein step S3 specifically comprises the following sub-steps
S3.1, calculating the period duration and the green light duration of the common signal, which comprises the following steps of
S3.1.1, calculating the period duration of a public signal, and determining an intersection signal control scheme by considering the layout and traffic flow factors of an intersection according to the traffic data of the intersection; then selecting the largest period in all intersections as a common signal period, and marking the intersection with the largest period as a key intersection:
wherein, C represents the period length of the common signal period, L represents the total loss time of the signal, and Y represents the sum of the key lane flow rate ratios of all phases;
s3.1.2 calculating the flow rate ratio y of the xth phase by performing phase green time distribution on the single-point intersection x According to the principle of 'balanced load' when the intersection signal is time, calculating the effective green time of the x-th phase:
wherein y is x A flow rate ratio representing the x-th phase;
s3.2, constructing a bus bidirectional green wave optimization model;
and S3.3, solving a bus bidirectional green wave optimization model.
In the foregoing method for generating bus green wave scheme robustness by considering travel time uncertainty, in step S3.1.2, the flow rate ratio y x The ratio of the actual or designed traffic volume of the key lane to the saturated flow rate is represented, the saturated flow rate adopts measured data, and if the saturated flow rate cannot be measured, the traffic flow of left turn and right turn is converted into equivalent straight traffic flow by using a straight equivalent method.
The foregoing method for generating public traffic green wave scheme robustness by considering travel time uncertainty, wherein step S3.2 specifically comprises the following sub-steps
S3.2.1 designing phase sequence, considering double-ring design with different left-turn and straight-turn of up-down, forming four phase sequence combinations with constraint conditions of phase sequence identical to those of the traditional MAXBOND model, and determining 0-1 variableThe values in the four phase sequence combinations are taken;
s3.2.2, the traditional MAXBOD model is improved by considering the uncertainty factor of the travel time of the bus, the initial queuing length of the social vehicle and the dissipation time, and a bus green wave optimization model is built:
solving for b i ，C，w i ，/>δ i ，/>Parameters;
the objective function is
The constraints are as shown in the following,
m i is an integer, b i ，C，w i ，/>δ i ，/>
t i For uncertainty variable, for a certain determined distribution, t i ～P(t)
For uncertainty variable, for a certain determined distribution, +.>
Wherein,and->The same variable in the up-down direction is indicated.
The foregoing method for generating public traffic green wave scheme robustness by considering travel time uncertainty, wherein step S3.3 specifically comprises the following sub-steps
S3.3.1, taking into account the solution constraints of the planning model, introducing 5 continuous variablesAnd 4 0-1 variables->Segmentation function P for the number of initial queuing vehicles i (T) transforming to obtain a linear function:
order the
Wherein,
wherein,
obtaining a bus green wave optimization linear model about bus travel time uncertainty;
s3.3.2, solving a bus green wave optimization model considering the uncertainty of the queuing and the travel time of the entrance way by adopting a genetic algorithm based on Monte Carlo simulation.
In step S3.3.2, the method for generating the bus green wave scheme robust by considering the uncertainty of the travel time solves a bus green wave optimization model by considering the queuing and the uncertainty of the travel time of the entrance lane, and comprises the following sub-steps
S3.3.2.1 simplified representation of a two-way bus green wave optimization model:
maxB＝F(b)＝E[f(b,t)]
s.t.g(b,t)≤0
h(b)≤0
b is a hypothetical decision variable, representing the bandwidth of the bidirectional public transport green wave; t is a random variable representing the travel time of the bus; g (b, t) represents the constraint condition containing random variables in the whole optimization model; h (b) represents a constraint that does not include a random variable;
s3.3.2.2 setting parameter values, inputting genetic algorithm including population scale NP, maximum evolution algebra NG, and crossover probability p c Probability of variation p m ；
S3.3.2.3, coding the problem to be optimized, and initializing to generate an initial population with NP chromosomes;
feasibility of chromosome testing using monte carlo simulation: sampling from probability distribution P (t) of random variable t to generate K groups of independent random variables t 1 ,t 2 ,…,t K Let K be ′ Is the number of times that the constraint condition g (b, t) is less than or equal to 0 in K times of sampling, repeatedly extracting K times, wherein alpha is the confidence level, ifThe chromosome is considered "viable" and otherwise "not viable";
s3.3.2.4 generating M groups of random numbers by utilizing Monte Carlo simulation, calculating fitness function values of chromosomes of each group of random numbers, stopping an algorithm if the evolution algebra meets the requirement of the maximum evolution algebra, and outputting an optimal solution; otherwise, the next step is carried out;
s3.3.2.5, selecting chromosomes from the initial population by adopting a genetic operator, forming a new population by using the chromosomes, and judging the feasibility of the chromosomes by operating the chromosomes through the crossover probability and the mutation probability;
s3.3.2.6 repeating steps S3.3.2.3 to S3.3.2.5 until the maximum evolution algebra is reached;
s3.3.2.6, outputting the optimal solution.
The beneficial effects of the invention are as follows:
(1) In the invention, the uncertainty distribution of the bus travel time is considered in the traditional MAXBOD model, so that the obtained green wave scheme can obtain better benefits after implementation, and the green wave scheme is embodied in indexes such as bus delay, parking times, parking passing proportion and the like;
(2) In the invention, a genetic algorithm based on Monte Carlo is adopted, so that the problem of uncertainty can be solved, and meanwhile, the advantages of two methods are considered: the Monte Carlo method considers high-dimensional random variables, and a genetic algorithm avoids the algorithm from falling into local optimum through a mutation mechanism, so that the searching capability is strong.
Drawings
FIG. 1 is an overall flow chart of an embodiment of the present invention;
FIG. 2 is a graph illustrating a piecewise function of the flow rate of vehicles exiting an upstream intersection in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of a downstream intersection red light queuing length model in accordance with an embodiment of the present invention;
FIG. 4 is a graph of time interval of a bi-directional green wave MAXBOD model in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a phase-sequence (lead or lag) combination in an embodiment of the invention;
FIG. 6 is a schematic diagram of a vehicle license plate recognition data sample according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a low frequency trajectory data sample of a bus in an embodiment of the present invention;
FIG. 8 is a schematic diagram of an original timing scheme of a signal control intersection in an embodiment of the present invention;
FIG. 9 is a schematic diagram of a partial bus track map matching result in an embodiment of the present invention;
FIG. 10 is a schematic diagram of a road segment bus travel time distribution in an embodiment of the present invention;
FIG. 11 is a schematic diagram of green wave period and relative phase difference at each intersection in an embodiment of the present invention;
FIG. 12 is a graph of results of bus and social vehicle assessment indicators under different scene bus green wave schemes in an embodiment of the invention;
FIG. 13 is a graph showing comparison of the delayed results of the conventional deterministic method and the method of the present embodiment for different time periods (peak and peaked) in the present embodiment;
FIG. 14 is a graph showing comparison of the results of the number of stops of the conventional deterministic method and the method of the present example at different time periods (peak and peaked) in the example of the present invention;
FIG. 15 is a graph showing comparison of the results of the conventional deterministic method and the parking ratio of the method according to the present embodiment at different time periods (peak and peaked peak).
Detailed Description
The method for generating the bus green wave scheme robust by considering the uncertainty of the travel time provided by the embodiment, as shown in fig. 1, comprises the following steps of
S1, acquiring traffic data related to public traffic positions, identity identification data, social vehicle traffic flow data, urban road network static data and the like, preprocessing the original data, analyzing distribution characteristics of the public traffic data on different road sections, acquiring public traffic flow information and travel time information of the public traffic between the road sections, and quantifying uncertainty of public traffic travel time of the road sections in a probability distribution mode.
Step S1 specifically comprises the following sub-steps
S1.1, collecting relevant traffic data, wherein the traffic data comprise public transportation position and identity recognition data, social vehicle traffic flow data and urban road network static data;
the bus position and identity recognition data comprise bus low-frequency track data, RFID radio frequency identification data and video bus perception data;
social vehicle traffic flow data, namely vehicle license plate data of video recognition, is used for providing driving information, and data fields comprise equipment numbers, dates, detection time, license plate numbers, vehicle types, license plate colors, entrance lane numbers and lane numbers;
the urban road network static data comprises vehicle detector equipment position data and related data of a signal control intersection;
s1.2, preprocessing bus position and identity data, carrying out map matching on longitude and latitude information and road network static information recorded by each data, extracting bus track points of each road section of a target trunk line, and obtaining the distribution condition of bus tracks;
s1.3, dividing low-frequency track data of the bus at time intervals of 1 hour, selecting a peak period with more track points for bus travel time uncertainty analysis, and finally quantifying the bus travel time uncertainty in the form of road section bus travel time probability distribution.
S2, considering traffic flow driving-away flow rates of traffic light states with different coordination phases of an upstream intersection, calculating initial queuing length of social vehicles during red light of the coordination phases of the downstream intersection according to the starting time difference of traffic light states of the upstream and downstream coordination phases, and calculating initial queuing dissipation time according to the saturated driving-away flow rates of the vehicles of the downstream intersection.
In step S2, the initial queuing length of the social vehicle during the phase red light of the coordination phase of the downstream intersection is calculated, comprising the following steps of
S2.1, initial queuing length of social vehicles during phase red light coordination of downstream intersections:
s2.1.1 assuming n signal control intersections on the main road, the number of the signal control intersections is denoted by i, i=1, 2,3, …, n, S i And S is i+1 Representing two signal control intersections numbered i and i+1 respectively, C representing signal coordination common period duration, r i And r i+1 Respectively represent intersections S i And S is i+1 Red light duration of straight phase g i And g i+1 Respectively represent intersections S i And S is i+1 Green light time length of straight phase; setting the vehicle to cross S i Travel to intersection S i+1 Is the upward direction from the intersection S i+1 Travel to intersection S i The direction of (2) is the downstream direction, and the upstream direction is described below as an example;
s2.1.2 calculating the vehicle arrival flow rate at the downstream intersection (intersection i+1) assuming that the vehicle arrives uniformly and there is no fleet dispersion:
the upstream intersection (intersection i) coordinates the phase to red:
the upstream intersection (intersection i) coordinates the phase to green:
wherein,and->Representing the arrival flow rate per lane (veh./sec./Ln.) at the downstream intersection (intersection i+1) during the coordination phase of the red and green lights at the upstream intersection (intersection i), respectively; />And->The traffic flow (Veh) reaching the downstream intersection (intersection i+1) during the period when the coordination phase of the upstream intersection (intersection i) is red light and green light is respectively represented;
representing the proportion of straight going to the downstream intersection of the traffic flow; r is (r) i Red light time(s) representing uncoordinated phase; c represents a trunk coordination common period(s); l (L) i+1 Representing the number of straight lanes of an entrance lane of a downstream intersection;
s2.1.3 simplifying the flow rate of vehicles exiting an upstream intersection into a piecewise function q i (t)，
The vehicle exit flow rate conditions during two cycles [0,2C ] are shown in fig. 2;
s2.1.4, calculating the green light start time difference of the upstream and downstream intersections to bePhase difference of upstream and downstream intersections>The expression (the value of 0 or more represents the followingThe start time of the red light phase is no later than the upstream intersection):
s2.1.5 taking into account the different arrival flow rates of vehicles at the upstream junction to the downstream junction and the phase difference between the upstream and downstream junctionsAnd the time difference of the green light start time +.>The headway of the vehicle is uniformly set to be h, and four different scenes are listed:
case one:
and a second case:
case three:
case four:
calculating and summarizing the initial queuing length of vehicles at the downstream intersection:
wherein,representing the initial queue length of the vehicles at the downstream intersection.
S2.2Calculating initial queuing dissipation time, assuming that vehicles are evenly released and evenly distributed on downstream lanes in red light and green light periods of the main road straight-going phase, and simultaneously recording the saturation flow rate of each lane in the green light period of the main road straight-going phase of a downstream intersection asThe arrival and departure of vehicles all adopt the idea of point queuing:
s2.2.1 calculating the upstream intersection (intersection i) in two cycles [0,2C]Flow rate q of vehicle exiting from inside i (t)，
S2.2.2, representing and calculating the number of vehicles queued at downstream intersectionsThe number of vehicles reaching the downstream intersection (intersection i+1) is equal to the number of vehicles exiting at different flow rates at the corresponding period of the upstream intersection (intersection i), and may be represented as a hatched area +_in fig. 3>Calculating the area of the shadow part through integration;
let P i (T) is t=0, t=t and the function q i (t)；
Calculation of
Wherein,representing the travel time(s) of the road section when the coordination phase of the downstream intersection is red;
calculating the initial queuing dissipation time of the downstream intersection (intersection i+1):
wherein τ i+1 Indicating the initial queuing dissipation time for the downstream intersection (intersection i+1).
S3, taking the downstream intersection coordination phase and the red light queuing dissipation time into consideration, inputting the bus travel time uncertainty distribution, improving a classical bus green wave optimization model MAXBOAND, converting the uncertainty model into a deterministic model through a random optimization means, and then solving the model.
S3.1, calculating the period duration and the green light duration of the common signal, which comprises the following steps of
S3.1.1, calculating the period duration of the common signal:
firstly, according to traffic data of an intersection, considering factors such as layout, traffic flow and the like of the intersection, and determining an intersection signal control scheme; then selecting the largest period in all intersections as a common signal period, and marking the intersection with the largest period as a key intersection:
where C represents the common signal period duration, L represents the total signal loss time, and Y represents the sum of the critical lane (group) flow rate ratios for all phases.
S3.1.2 calculating the flow rate ratio y of the xth phase by performing phase green time distribution on the single-point intersection x Flow rate ratio y x Representing the ratio of the actual or designed traffic volume of the lane(s) to the saturation flow rate; the saturated flow rate adopts measured data, if the saturated flow rate cannot be measured, straight running is usedThe equivalent method is used for converting the traffic flow of left turn and right turn into equivalent straight traffic flow;
according to the principle of 'balanced load' when the intersection signals are time, calculating effective green and other time of the x-th phase:
wherein y is x Critical lane (group) flow ratio representing the x-th phase.
S3.2, constructing a bus bidirectional green wave optimization model: the classical MAXBAND algorithm is based on the fact that the distance between adjacent intersection sections, the travelling speed threshold value and the timing scheme of each intersection signal control are all determined to be the situation, the objective function is that the sum of the uplink and downlink green wave bandwidths of each intersection is maximum, the model time interval diagram is shown in fig. 4, and the meaning of parameters related to MAXBAND modeling is as follows:
b represents an objective function, which is an expected value of distribution obeyed by a weighted average value of the two-way public traffic green wave bandwidth of each road section;
indicating the intersection S in the upward (downward) direction i (S i+1 ) To intersection S i+1 (S i ) Is a bus green wave bandwidth;
representing disturbance variables, intersection S i The interval time between the right side (left side) of the upper (lower) row red light and the left side (right side) of the green wave band;
k i the green wave bandwidth demand ratio of the downlink direction and the uplink direction of the intersection i is expressed, and the green wave bandwidth demand ratio is usually the ratio of the bus flow in the downlink direction and the bus flow in the uplink direction;
indicating intersection S i When red light in the up (down) directionA compartment;
indicating intersection S i Green time in the up (down) direction of (a) a row;
indicating intersection S i Left turn green time in the up (down) direction of (a) a;
c represents the period length of a common signal period of each intersection on a green wave optimization control road section of a main road;
Δ i indicating intersection S i When in bidirectional discharge, the interval time between the midpoint of the uplink red light time and the midpoint of the downlink latest period red light time is longer than the interval time between the midpoint of the uplink red light time and the midpoint of the downlink latest period red light time;
representing a 0-1 variable, and introducing the variable can combine the phase-phase constraint condition relation into a general formula;
indicating intersection S i Time midpoint of up (down) red light and intersection S i+1 Interval time of midpoint of the up (down) line red light time;
indicating that the vehicle is at two intersections S i 、S i+1 Travel time of the up (down) row in between; />Representing bus slave intersection S i (S i+1 ) Is driven to the intersection S by the parking line of (1) i+1 (S i ) Is a travel time of the tail of the ingress lane queue.
Step S3.2 specifically comprises the following sub-steps
S3.2.1, consider the different double-ring designs (lead or lag) of going up and down left-turn and going straight, the constraint condition of the phase sequence is the same as that of the traditional MAXBOAND model, the combined schematic diagram of four phase sequences is shown in FIG. 5; determining 0-1 variableThe values of which under the combination of four phase sequences are shown in table 1;
TABLE 1 phase sequence combinationsIs of the value of (2)
S3.2.2, the traditional MAXBOD model is improved by considering the uncertainty factor of the travel time of the bus and the initial queuing length and the dissipation time of the social vehicle obtained in the step S2, and a bus green wave optimization model is built:
solving: b i ，C，w i ，/>δ i ，/>Parameters;
the objective function is
The constraints are as follows:
/>
m i is an integer, b i ，C，w i ，/>δ i ，/>
t i For uncertainty variable, for a certain determined distribution, t i ～P(t)
For uncertainty variable, for a certain determined distribution, +.>
Wherein,and->The same variable in the up-down direction is indicated.
S3.3, solving a bus bidirectional green wave optimization model, which specifically comprises the following substeps of
S3.3.1, taking into account the solution constraints of the planning model, introducing 5 continuous variablesAnd 4 0-1 variables->Segmentation function P for the number of initial queuing vehicles i (T) transforming to obtain a linear function:
order the
Wherein,
wherein,
and obtaining a bus green wave optimization linear model about the uncertainty of the bus travel time.
S3.3.2, solving a bus green wave optimization model considering the uncertainty of the queuing and the travel time of an entrance way by adopting a genetic algorithm based on Monte Carlo simulation, wherein the solving process is as follows:
s3.3.2.1 simplified representation of a two-way bus green wave optimization model:
maxB＝F(b)＝E[f(b,t)]
s.t.g(b,t)≤0
h(b)≤0
b is a hypothetical decision variable, representing the bandwidth of the bidirectional public transport green wave; t is a random variable representing the travel time of the bus; g (b, t) represents the constraint condition containing random variables in the whole optimization model; h (b) represents a constraint that does not include a random variable;
s3.3.2.2 setting parameter values, inputting genetic algorithm including NP (population size), NG (maximum evolution algebra), p c (crossover probability) and p m (probability of variation);
s3.3.2.3, coding the problem to be optimized, and initializing to generate an initial population with NP chromosomes;
feasibility of chromosome testing using monte carlo simulation: sampling from probability distribution P (t) of random variable t to generate K groups of independent random variables t 1 ,t 2 ,…,t K Let K be ′ Is the number of times that the constraint condition g (b, t) is less than or equal to 0 in K times of sampling, repeatedly extracting K times, wherein alpha is the confidence level, ifThe chromosome is considered "viable", otherwise "not viable"；
S3.3.2.4 generating M groups of random numbers by utilizing Monte Carlo simulation, calculating fitness function values (objective function values) of chromosomes of each group of random numbers, and if the evolution algebra meets the maximum evolution algebra requirement, stopping the algorithm and outputting an optimal solution; otherwise, the next step is carried out;
s3.3.2.5, selecting chromosomes from the initial population by adopting a genetic operator, forming a new population by using the chromosomes, and judging the feasibility of the chromosomes by operating the chromosomes through the crossover probability and the mutation probability;
s3.3.2.6 repeating steps S3.3.2.3 to S3.3.2.5 until the maximum evolution algebra is reached;
s3.3.2.6, outputting the optimal solution.
The following specific cases are used for analyzing the robust generation method of the public traffic green wave scheme taking the uncertainty of the travel time into consideration.
1. In the step S1, social vehicle traffic flow data are shown in fig. 6, bus position and identity recognition data are shown in fig. 7, intersection signal timing data are shown in fig. 8, bus track map matching results are shown in fig. 9, and bus travel time distribution is shown in fig. 10;
2. in step S3, the green wave period and the relative phase difference of each intersection are shown in FIG. 11;
3. using SUMO simulation to evaluate a bus green wave optimization model, wherein the evaluation result is shown in FIG. 12;
4. and verifying the running effect of the bus green wave scheme considering the uncertainty of the bus running time, and comparing and analyzing simulation results of a plurality of buses according to scenes, wherein the results are shown in fig. 13-15.
The uncertainty distribution of bus travel time is considered in the traditional MAXBOD model, so that the obtained green wave scheme can obtain better benefits after implementation, and the green wave scheme is particularly embodied in indexes such as bus delay, parking times, parking passing proportion and the like; the genetic algorithm based on Monte Carlo is adopted, so that the problem of uncertainty can be solved, and meanwhile, the advantages of two methods are considered: the Monte Carlo method considers high-dimensional random variables, and a genetic algorithm avoids the algorithm from falling into local optimum through a mutation mechanism, so that the searching capability is strong.
In addition to the embodiments described above, other embodiments of the invention are possible. All technical schemes formed by equivalent substitution or equivalent transformation fall within the protection scope of the invention.
Claims (2)
1. A robust generation method of a public traffic green wave scheme considering travel time uncertainty is characterized by comprising the following steps of: comprises the following steps
S1, acquiring traffic data, wherein the traffic data comprises bus position and identity identification data, social vehicle traffic flow data and urban road network static data, preprocessing the original data, analyzing the distribution characteristics of the bus data in different road sections, acquiring bus flow information and travel time information of buses in road sections, and quantifying the uncertainty of the bus travel time in the road sections in a probability distribution mode;
step S1 specifically comprises the following sub-steps
S1.1, collecting relevant traffic data, wherein the traffic data comprise public transportation position and identity recognition data, social vehicle traffic flow data and urban road network static data;
s1.2, preprocessing bus position and identity identification data, carrying out map matching on longitude and latitude information and road network static information recorded by each data, extracting bus track points of each road section of a target trunk line, and obtaining the distribution condition of bus tracks;
s1.3, dividing low-frequency track data of a bus by taking 1 hour as a time interval, selecting a peak period with more track points for carrying out bus travel time uncertainty analysis, and finally quantifying the bus travel time uncertainty in the form of road section bus travel time probability distribution;
s2, considering traffic flow driving-away flow rates of traffic light states with different coordination phases of an upstream intersection, calculating initial queuing length of social vehicles during red light of the coordination phases of the downstream intersection according to the starting time difference of traffic light states of the upstream and downstream coordination phases, and calculating initial queuing dissipation time according to the traffic flow driving-away flow rates of the downstream intersection;
in step S2, the initial queuing length of the social vehicle during the phase red light of the coordination phase of the downstream intersection is calculated, comprising the following steps of
S2.1.1 assuming that there are n signalized intersections on the thoroughfare, the signalized intersections are numbered i, i=1, 2,3 i And S is i+1 Two signal control intersections with the numbers of i and i+1 are respectively represented, C represents the common period duration of signal coordination, and r i And r i+1 Respectively represent intersections S i And S is i+1 Red light duration of straight phase g i And g i+1 Respectively represent intersections S i And S is i+1 Green light time length of straight phase; setting the vehicle to cross S i Travel to intersection S i+1 Is the upward direction from the intersection S i+1 Travel to intersection S i The direction of (2) is the downlink direction;
s2.1.2, assuming that the vehicle arrives uniformly and there is no fleet dispersion, calculate the vehicle arrival flow rate at the downstream intersection, intersection i+1:
the upstream intersection, i.e. intersection i coordinates the phase to red:
the coordination phase of the upstream intersection, namely intersection i, is green light:
wherein,and->Each lane of the intersection i+1, which respectively represents the upstream intersection, i.e. intersection i, reaching the downstream intersection during the period of the coordination phase of red light and green lightThe arrival flow rate in veh/sec/Ln.; />And->The vehicle flow reaching the downstream intersection, namely intersection i+1, respectively represents the vehicle flow reaching the downstream intersection, namely intersection i, in the period of the red light and the green light of the coordination phase of intersection i, and the unit is Veh;
representing the proportion of straight going to the downstream intersection of the traffic flow; ri represents the red light time of the uncoordinated phase in s; c represents a trunk coordination common period, and the unit is s; l (L) i+1 Representing the number of straight lanes of an entrance lane of a downstream intersection;
s2.1.3 simplifying the flow rate of vehicles exiting an upstream intersection into a piecewise function q i (t) two periods [0,2C ]]The vehicle exit flow rate conditions within can be expressed as:
s2.1.4, calculating a green light starting time difference of an upstream and downstream intersection:
wherein,the green light starting time difference of the upstream and downstream intersections is represented; />Indicating the phase difference between the upstream and downstream intersections, when the value is 0 or moreRepresenting that the phase start time of the downstream red light is not later than that of the upstream intersection;
s2.1.5 taking into account the different arrival flow rates of vehicles at the upstream junction to the downstream junction and the phase difference between the upstream and downstream junctionsAnd the time difference of the green light start time +.>The headway of the vehicle is uniformly set to be h, and four different scenes are listed:
case one:
and a second case:
case three:
case four:
calculating and summarizing the initial queuing length of vehicles at the downstream intersection:
wherein,representing an initial queuing length of vehicles at a downstream intersection;
in step S2, it is assumed that the vehicles are uniformly released and uniformly split in red and green light periods of the road straight phaseDistributed on the downstream lanes, and the saturation flow rate of each lane in the green light period of the straight-going phase of the main road of the downstream intersection is recorded asThe vehicle arrival and departure all adopt the idea of point queuing, and calculate the initial queuing dissipation time, comprising the following steps of
S2.2.1, calculating the vehicle flow rate out of the upstream intersection, i.e. intersection i, in two cycles [0,2C ],
wherein q i (t) represents the upstream intersection, i.e. intersection i in two cycles [0,2C ]]The vehicle flow rate of the inner drive-out;
s2.2.2, representing and calculating the number of vehicles queued at downstream intersectionsThe number of vehicles arriving at the downstream intersection, i.e. intersection i +1, is equal to the number of vehicles exiting at different flow rates at the upstream intersection, i.e. intersection i, for a corresponding period of time,
wherein,the travel time of a road section when the coordination phase of a downstream intersection is red light is represented, and the unit is s;
calculating the initial queuing dissipation time of the downstream intersection, namely intersection i+1:
wherein τ i+1 Representing the initial queuing dissipation time for the downstream intersection, i.e., intersection i+1;
s3, considering the downstream intersection coordination phase and the red light queuing dissipation time, inputting the bus travel time uncertainty distribution, improving a classical bus green wave optimization model MAXBOAND, converting the uncertainty model into a deterministic model through a random optimization means, and then solving the model;
step S3 specifically comprises the following sub-steps
S3.1, calculating the period duration and the green light duration of the common signal, which comprises the following steps of
S3.1.1, calculating the period duration of a public signal, and determining an intersection signal control scheme by considering the layout and traffic flow factors of an intersection according to the traffic data of the intersection; then selecting the largest period in all intersections as a common signal period, and marking the intersection with the largest period as a key intersection:
wherein, C represents the period length of the common signal period, L represents the total loss time of the signal, and Y represents the sum of the key lane flow rate ratios of all phases;
s3.1.2 calculating the flow rate ratio y of the xth phase by performing phase green time distribution on the single-point intersection x According to the principle of 'balanced load' when the intersection signal is time, calculating the effective green time of the x-th phase:
wherein y is x A flow rate ratio representing the x-th phase; flow ratio y x The ratio of the actual or designed traffic volume of the key lane to the saturated flow rate is represented, the saturated flow rate adopts measured data, if the saturated flow rate cannot be measured, a straight-going equivalent method is used for converting the traffic flow of left turn and right turn into equivalent straight-going traffic flow;
s3.2, constructing a bus bidirectional green wave optimization model;
step S3.2 specifically comprises the following sub-steps
S3.2.1 designing phase sequence, considering double-ring design with different left-turn and straight-turn of up-down, forming four phase sequence combinations with constraint conditions of phase sequence identical to those of the traditional MAXBOND model, and determining 0-1 variableThe values in the four phase sequence combinations are taken;
s3.2.2, the traditional MAXBOD model is improved by considering the uncertainty factor of the travel time of the bus, the initial queuing length of the social vehicle and the dissipation time, and a bus green wave optimization model is built:
solving for b i ，C，w i ，/>δ i ，/>Parameters;
the objective function is
The constraints are as shown in the following,
m i is an integer, b i ，C，w i ，/>δ i ，/>
t i For uncertainty variable, for a certain determined distribution, t i ～P(t)
For uncertainty variable, for a certain determined distribution, +.>
Wherein,and->The same variable representing the uplink and downlink directions;
s3.3, solving a bus bidirectional green wave optimization model;
step S3.3 specifically includes the following sub-steps
S3.3.1, taking into account the solution constraints of the planning model, introducing 5 continuous variablesAnd 4 0-1 variables->Segmentation function P for the number of initial queuing vehicles i (T) transforming to obtain a linear function:
order the
Wherein,
wherein,
obtaining a bus green wave optimization linear model about bus travel time uncertainty;
s3.3.2, solving a bus green wave optimization model considering the uncertainty of the queuing and the travel time of the entrance way by adopting a genetic algorithm based on Monte Carlo simulation;
in step S3.3.2, a bus green wave optimization model is solved which considers the uncertainty of the queuing and the travel time of the entrance way, and the method comprises the following substeps
S3.3.2.1 simplified representation of a two-way bus green wave optimization model:
max B＝F(b)＝E[f(b，t)]
s.t.g(b，t)≤0
h(b)≤0
b is a hypothetical decision variable, representing the bandwidth of the bidirectional public transport green wave; t is a random variable representing the travel time of the bus; g (b, t) represents the constraint condition containing random variables in the whole optimization model; h (b) represents a constraint that does not include a random variable;
s3.3.2.2 setting parameter values, inputting genetic algorithm including population scale NP, maximum evolution algebra NG, and crossover probability p c Probability of variation p m ；
S3.3.2.3, coding the problem to be optimized, and initializing to generate an initial population with NP chromosomes;
feasibility of chromosome testing using monte carlo simulation: sampling from probability distribution P (t) of random variable t to generate K groups of independent random variables t 1 ，t 2 ，...，t K Let K' be the number of times satisfying constraint g (b, t) less than or equal to 0 in K samples, repeatedly extracting K times, alpha as confidence level, ifThe chromosome is considered "viable" and otherwise "not viable";
s3.3.2.4 generating M groups of random numbers by utilizing Monte Carlo simulation, calculating fitness function values of chromosomes of each group of random numbers, stopping an algorithm if the evolution algebra meets the requirement of the maximum evolution algebra, and outputting an optimal solution; otherwise, the next step is carried out;
s3.3.2.5, selecting chromosomes from the initial population by adopting a genetic operator, forming a new population by using the chromosomes, and judging the feasibility of the chromosomes by operating the chromosomes through the crossover probability and the mutation probability;
s3.3.2.6 repeating steps S3.3.2.3 to S3.3.2.5 until the maximum evolution algebra is reached;
s3.3.2.6, outputting the optimal solution.
2. The method for generating the public traffic green wave scheme robustness taking into account the uncertainty of the travel time according to claim 1, wherein the method comprises the following steps of: in the step S1.1, the bus position and identity recognition data include bus low-frequency track data, RFID radio frequency identification data and video bus perception data;
social vehicle traffic flow data representing vehicle license plate data identified through video to provide passing information, wherein data fields of the passing information comprise equipment numbers, dates, detection time, license plate numbers, vehicle types, license plate colors, entrance lane numbers and lane numbers;
the urban road network static data includes vehicle detector device location data and signal control intersection related data.
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