A method for calculating green wave coordinated clearing time based on sampled trajectory data

Abstract

The present invention discloses a green wave coordinated clearing time calculation method based on sampling trajectory data, comprising the following steps: 1) for the green wave coordinated clearing time from the upstream intersection I i To downstream intersection I j The coordination path P (i→j) , the classification definition and its related traffic flow f S(i→j) , detect sampling vehicle n at downstream intersection I j Parking status S (n,j) and parking time T S(n,j) , calculate its actual passing time T P(n,j) and ideal passing time T E(n,j) , calculate the number of arriving vehicles by combining the sampled vehicle trajectories; 2) construct the traffic flow f in each direction S(i→j) The arrival likelihood function estimates the traffic flow f in each direction S(i→j) The arrival flow rate λ S(i→j) ; 3) Calculate the maximum clearing time of all non-target coordinated traffic #imgabs0#4) Determine the target coordinated vehicle at the downstream intersection I according to the green light duration and maximum clearing time of the upstream and downstream intersection coordination phase #imgabs1# j The passing situation of the target coordinated traffic flow f is calculated O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t C(i→j) The present invention can effectively calculate the clearance time of the target coordinated vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic signal control, and more particularly to a method for calculating green wave coordinated clearing time based on sampled trajectory data. Background Art

[0002] Currently, green wave coordination design approaches for urban roads can be categorized into three types: head-of-the-line, mid-of-the-line, and tail-of-the-line. When head-of-the-line green wave coordination is employed, it is necessary to calculate the queue clearance time for the target coordinated vehicle. However, limited research exists on how to calculate the target coordinated vehicle clearance time by estimating the arrival flow rates of coordinated vehicles from various directions based on sampled trajectory data. With the increasing number of connected vehicles in today's society, trajectory data has become even more valuable for research.

[0003] To address this issue, the present invention provides a method for calculating the green wave coordination clearance time based on sampled vehicle trajectory data. This method classifies and defines the various directional traffic flows associated with the coordination path, calculates the actual and ideal passing times of the sampled vehicles at the downstream intersection, calculates the number of arriving vehicles based on the sampled vehicle trajectories, constructs an arrival likelihood function, estimates the arrival flow rate of each directional traffic flow, calculates the maximum clearance time of all non-target coordinated traffic flows, and determines the passage of the target coordinated vehicle at the downstream intersection based on the green light duration of the coordinated phase at the upstream and downstream intersections. Finally, the clearance time of the target coordinated vehicle is calculated. Summary of the Invention

[0004] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the existing technology and provide a method for calculating the green wave coordinated clearance time based on sampled trajectory data. Based on the analysis of the green wave coordinated control demand characteristics, the present invention provides the definition and calculation method of the relevant parameters of the sampled trajectory vehicle coordinated control, providing a research idea for arrival flow rate estimation.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention provides a method for calculating green wave coordinated clearing time based on sampled trajectory data, comprising the following steps:

[0007] S1, for the upstream intersection I i To downstream intersection I j The coordination path P (i→j) , the classification definition and its related traffic flow f S(i→j) , detect sampling vehicle n at downstream intersection I j Parking status S (n,j) and parking time T S(n,j) , calculate its actual passing time T P(n,j) and ideal passing time T E(n,j) , the number of arriving vehicles is calculated by combining the sampled vehicle trajectories;

[0008] S2. Construct the traffic flow f in each direction S(i→j) The arrival likelihood function estimates the traffic flow f in each direction S(i→j) The arrival flow rate λ S(i→j) ;

[0009] S3. Calculate the maximum clearing time of all non-target coordinated traffic flows

[0010] S4. Coordinate the phase green light duration and maximum clearing time according to the upstream and downstream intersections Determine the target coordinated vehicle at the downstream intersection I j The passing situation of the target coordinated traffic flow f is calculated O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t C(i→j) .

[0011] As a preferred technical solution, the step S1 is specifically as follows:

[0012] S101, for the upstream intersection I i To downstream intersection I j The coordination path P (i→j) , the classification definition and its related traffic flow f S(i→j) ;

[0013] When the upstream intersection I i To downstream intersection I j Coordination path P (i→j) The goal of coordinating traffic flow f O(i→j) When designing the green wave coordination of the first type of team, the target coordinated traffic flow f under different circumstances O(i→j) , non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) and f 3(i→j) The definition and selection of are shown in Table 1, where f (i,x→j,y) From upstream intersection I i The x-flow direction enters and downstream intersection I j The y-flow direction of the vehicle leaving, when x is 1, 2, 3, it represents the vehicle flow from the upstream intersection I i Turn left, go straight, turn right and go to the downstream intersection I j When y is 1, 2, or 3, it means that the traffic flow turns left, goes straight, or turns right to leave the downstream intersection I at the current entrance. j , traffic flow (i,x→j,D) The D direction is the target coordinated traffic flow f O(i→j) At downstream intersection I j The departure direction of the dedicated lane, the traffic flow f (i,x→j,D′) The D′ direction is the target coordinated vehicle flow f O(i→j)At downstream intersection I j All departure directions of the shared lane, define a Boolean variable F R(i→j) To determine the upstream intersection I i Define the Boolean variable F to determine whether the right-turn merging traffic is controlled. C(i→j) To determine the downstream intersection I j Coordinate whether the entrance lane of the route is a shared lane;

[0014] Table 1 Definitions of target coordinated traffic flow and non-target coordinated traffic flow in different situations

[0015]

[0016] Target coordinated traffic flow f O(i→j) The arrival flow rate is denoted as λ O(i→j) , non-target coordinated traffic flow f 1(i→j) and f 2(i→j) The arrival flow rate is denoted as λ 1(i→j) and λ 2(i→j) ; If the upstream intersection I i The right-turn merging traffic flow is not controlled by signals, so it is defined as non-target coordinated traffic flow f 3(i→j) The arrival flow rate λ 3(i→j) Perform calculations;

[0017] According to the periodic signal phase setting, the traffic flow in the period is merged into the downstream intersection I j There are two situations: one is the upstream intersection I i The signal phase is set so that there is no mixing between vehicles entering the downstream intersection I j The second is the upstream intersection I i The signal phase setting allows for mixed traffic flow into the downstream intersection I j phenomenon;

[0018] When the upstream intersection I i The signal phase is set so that there is no mixing between vehicles entering the downstream intersection I j When the phenomenon occurs, estimate the target coordinated traffic flow f O(i→j) Coordinate traffic flow with non-target f 1(i→j) 、f 2(i→j) Vehicle arrival situation; when the upstream intersection I i The signal phase setting makes the traffic flow mixed into the downstream intersection I j If the traffic flow released in the same period is non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) or f 3(i→j) Two of them are merged and recorded as non-target coordinated merged traffic flow f 4(i→j) , whose arrival flow rate is λ 4(i→j), if the released traffic flow exists in the same period and there is a target coordinated traffic flow f O(i→j) There are also other non-target coordinated traffic flows f 1(i→j) 、f 2(i→j) or f 3(i→j) , merge them into the target coordinated update traffic flow f H(i→j) , whose arrival flow rate is λ H(i→j) ;

[0019] According to the above traffic flow division results, the coordinated path P (i→j) The relevant traffic flows in each direction are defined as f S(i→j) , S takes O, H, 1, 2, 3, 4, representing the target coordinated traffic flow f O(i→j) , target coordinated update traffic flow f H(i→j) , non-target coordinated traffic flow f 1(i→j) , non-target coordinated traffic flow f 2(i→j) , non-target coordinated traffic flow f 3(i→j) and non-target coordinated merging of traffic flows f 4(i→j) ;

[0020] S102, detecting sampling vehicle n at downstream intersection I j Parking status S (n,j) and parking time T S(n,j) , calculate its actual passing time T P(n,j) and ideal passing time T E(n,j) ; If the sampling vehicle n is at the downstream intersection I j There is a parking state, parking state S (n,j) Take 1, otherwise take 0; if the parking state S of the sampled vehicle n (n,j) is 0, based on the sampling vehicle n at intersection I j The detection time of the trajectory points before and after the stop line and the distance between them and the stop line are calculated by interpolation method to calculate the actual time when the sampled vehicle n passes through the intersection I j The actual passing time T of the stop line at the entrance of the coordinated path P(n,j) ; If the parking state S of the sampled vehicle n (n,j) is 1, detecting the sampling vehicle n at intersection I j The first stop time T of the coordinated path entrance S(n,j) , calculate the ideal number of sampled vehicles n passing through intersection I j The ideal passing time T of the stop line at the entrance of the coordinated path E(n,j) , as shown in formula (1):

[0021]

[0022] Where D (n,j) is the sampling vehicle n at intersection I jThe distance between the coordinated path entrance lane and the stop line when the first stop is made, is the average ideal driving speed of the vehicle on the current road section;

[0023] S103, according to the flow of traffic f S(i→j) Arrival situation: calculate the number of arriving vehicles; discretize the continuous arrival time within the signal period with a time interval of k seconds. m The number of vehicles that ideally pass the intersection stop line during the time period is counted, and the vehicle arrival ratio P(t m ) is shown in formula (2):

[0024]

[0025] Where N is the total number of vehicles whose trajectories can be collected, m is the time interval number after discrete processing, and F{T E(n,j) =t m} is the ideal passing time T for judging the collection of any sampled vehicle n E(n,j) Is it in t m The indicator variable within a period is calculated as shown in formula (3):

[0026]

[0027] Traverse the sampled trajectory data of the S-flow vehicle flow, and sequentially include the vehicle trajectory into the data set of different situations U. S takes O, H, 1, 2, 3, and 4, which represent the target coordinated vehicle flow f respectively. O(i→j) , target coordinated update traffic flow f H(i→j) , non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) 、f 3(i→j) and non-target coordinated merging of traffic flows f 4(i→j) , case U takes 1, 2, 3, case 1 corresponds to the first sampling vehicle n in all S-flow traffic within the signal period A The driving trajectory is the parking trajectory. Case 2 corresponds to the preceding vehicle of two adjacent sampled vehicles in the S-flow traffic flow within the signal period. With the following car n B The driving trajectories are all parking trajectories. Case 3 corresponds to the preceding vehicle of two adjacent sampled vehicles in the S-flow traffic flow within the signal period. With the following car n C The driving trajectories are divided into parking trajectories and non-parking trajectories; other sampling trajectory collection situations cannot provide effective information for traffic arrival flow rate estimation and are therefore not considered;

[0028] For the sampled trajectory data of the S-flow traffic, all trajectory data that meet the condition 1 are recorded as set A, and any sampled vehicle n in set A is recorded as AThe estimated number of arriving vehicles is recorded as All trajectory data that meet condition 2 are recorded as set B, and any adjacent sampled vehicle in set B is With n B The estimated number of arriving vehicles is recorded as All trajectory data that meet condition 3 are recorded as set C, and any adjacent sampled vehicle in set C is With n C The estimated number of arriving vehicles is recorded as

[0029] As a preferred technical solution, in step S103, for any sampled vehicle n in set A, A , estimate the red light turn-on time of the coordinated phase The ideal passing time with the current vehicle trajectory Downstream Intersection I j The number of arriving vehicles corresponding to the coordinated path As shown in formula (4) and formula (5):

[0030]

[0031]

[0032]

[0033] In the formula, [] is the rounding operator, To coordinate the phase green light on time To sample vehicle n A Actual passing time The length of the green light between and are the sampled vehicles n A Pass downstream intersection I j The time corresponding to the red and green lights turning on in the coordinated phase, h s is the saturation headway, Poisson represents the number of arriving vehicles Obeying Poisson distribution, λ S(i→j) is the arrival flow rate of traffic to S, The number of vehicles arriving The corresponding time interval.

[0034] As a preferred technical solution, in step S103, for any adjacent sampled vehicles in set B, With n B , estimating the sampled vehicles Ideal passing time With sampled vehicles n B Ideal passing time Number of vehicles arriving between The calculation satisfies formula (7):

[0035]

[0036]

[0037]

[0038] Where, For sampling vehicles Actual passing time With sampled vehicles n B Actual passing time The length of the green light between The number of vehicles arriving The corresponding time interval.

[0039] As a preferred technical solution, in step S103, for any adjacent sampled vehicles in set C, With n C , estimating the sampled vehicles Ideal passing time With sampled vehicles n C Actual passing time Number of vehicles arriving between The calculation satisfies formula (10):

[0040]

[0041]

[0042]

[0043] Where, For sampling vehicles Actual passing time With sampled vehicles n C Actual passing time The length of the green light between The number of vehicles arriving The corresponding time interval.

[0044] As a preferred technical solution, step S2 is specifically as follows:

[0045] S201, construct the traffic flow f in each direction S(i→j) The arrival likelihood function of

[0046] According to the three sets obtained in step S103, the arrival likelihood function for the S-flow traffic is constructed as shown in formula (13), where h is any adjacent sampled vehicle in set C. With nC The number of vehicles that may exist between the two, the maximum value is the number of arriving vehicles There are a total of a sampled vehicle trajectories collected in set A, b pairs of adjacent sampled vehicle trajectories collected in set B, and c pairs of adjacent sampled vehicle trajectories collected in set C;

[0047]

[0048] S202: Estimating the traffic flow f in each direction S(i→j) The arrival flow rate λ S(i→j) Based on Bayesian inference, we take the exponential distribution as the arrival flow rate λ S(i→j) The prior distribution of the exponential distribution parameter β S(i→j) is a known empirical value, and the arrival flow rate λ S(i→j) is a positive value, then the arrival flow rate λ S(i→j) The prior distribution function and posterior probability expressions of are shown in formula (14) and formula (15):

[0049]

[0050]

[0051] The maximum a posteriori estimation method is used to estimate the parameters, using the parameter variable value when the posterior probability is the maximum as the point estimate of the parameter. The denominator P(D) of the posterior probability is a constant value and will not affect the maximum value point of the posterior probability function, so it is ignored. The posterior probability function is logarithmically processed and simplified to obtain formula (16):

[0052]

[0053] for The estimated value of is calculated by equation (17), where l represents the number of loop iterations and g represents any adjacent sampled vehicle in the set C. With n C The maximum number of vehicles that may exist between

[0054]

[0055] The flow S is directed to the vehicle flow reaching the flow rate λ S(i→j) The average value of the arrival flow rate is recorded as the average arrival flow rate The maximum a posteriori estimation method is combined with formula (17) and iterative calculation is performed until convergence, and the result satisfies formula (18):

[0056]

[0057] As a preferred technical solution, step S202 further includes the following:

[0058] The average arrival flow rate will converge Multiply it by the vehicle arrival ratio to obtain the traffic flow f in each direction S(i→j) Arrival flow rate λ at different time periods S(i→j) The expression satisfies formula (19):

[0059]

[0060] As a preferred technical solution, step S3 is specifically as follows:

[0061] S301. Calculate the maximum clearing time of all non-target coordinated traffic flows The time for clearing all non-target coordinated traffic flows is defined as t CT ( i→j ), is the maximum clearing time of all non-target coordinated traffic flows, calculated to satisfy formula (20); t S(i→j) Corresponding to the traffic flow f in each direction S(i→j) The time it takes for queued vehicles to be cleared, The clearing time t S(i→j) The maximum clearing time to reach the maximum value, Q S(i→j) Corresponding to the traffic flow f in each direction S(i→j) The number of vehicles in the queue, is the number of vehicles in the queue Q S(i→j) The maximum number of vehicles in the queue that reaches the maximum value, S (i→j) Coordinate traffic flow for the goal f O(i→j) Or target coordinated update of traffic flow f H(i→j) The sum of the saturated flows of all lanes in the corresponding coordinated path, the Boolean variable F S(i→j) Used to determine whether there is traffic flow f in the traffic flow division result S(i→j) , there is traffic flow f S(i→j) Then F S(i→j) Takes 1, otherwise takes 0, where S takes O, H, 1, 2, 3, 4;

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] Where, is the average starting loss time of vehicles in the queue, T 1,j With T 2,j For non-target coordinated traffic flow f 1(i→j)With f 2(i→j) The first vehicle ideally passes downstream intersection I j The ideal time to pass the stop line, T F,j Coordinate traffic flow for the goal f O(i→j) Or target coordinated update of traffic flow f H(i→j) The first vehicle ideally passes downstream intersection I j Ideal time to pass the stop line, T 4,j For non-target coordinated merging of traffic flows f 4(i→j) The first vehicle ideally passes downstream intersection I j Ideal time to pass the stop line, T O,j With T E,j Downstream intersection I j The start and end time points of the target coordination phase cycle, T D,j Downstream intersection I j The green light end time of the coordination phase.

[0068] As a preferred technical solution, step S4 is specifically as follows:

[0069] S401, based on the green light duration of the upstream and downstream intersections and the maximum clearing time of all non-target coordinated traffic flows Determine the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) At downstream intersection I j The passing situation of the downstream intersection I j Green light duration t of the coordinated phase G(j→k) Coordinate the maximum clearing time of traffic with all non-target vehicles and upstream intersection I i Green light duration t of the coordinated phase G(i→j) If the sum of Then the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) If all vehicles in the queue can pass through the downstream intersection in the current cycle and all vehicles in the queue are composed of non-target coordinated traffic, go to step S402; Then the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Unable to pass through the downstream intersection in the current cycle, the queued vehicles are divided into non-target coordinated traffic flow and target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Composition, go to step S403;

[0070] S402, calculate the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t in case of parking and waitingC(i→j) ; define t C(i→j) From upstream intersection I i To downstream intersection I j The goal of coordinating traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) The time required to clear the queued vehicles when the head-of-line green wave coordination design is adopted; if there is no target coordinated vehicle to stop and wait, then t C(i→j) =t CT ( i→j) ; Define Boolean variable F AS ( i→j) To determine the traffic flow f S(i→j) Whether the release phase follows the target coordination phase, if so, the Boolean variable F AS(i→j) Take 1, otherwise take 0, where S takes 1, 2, 4; F AS(i→j) Satisfying formula (25):

[0071] F A1(i→j) +F A2(i→j) +F A4(i→j) ≤1 (25)

[0072] If F A1(i→j) +F A2(i→j) +F A4(i→j) =1, then there is a non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) Or non-target coordinated merging of traffic flows f 4(i→j) The release phase follows the target coordination phase, and some non-target coordinated vehicles pass through the downstream intersection I in the current cycle. j The green light duration of the traffic flow that is allowed to pass through the downstream intersection in the current cycle is defined as t T(i→j) , satisfying formula (26); at this time, the clearing time t C(i→j) Satisfy formula (27);

[0073] t T(i→j) =t G(j→k) -t G(i→j) -t C(i→j) (26)

[0074]

[0075] Where Q T(i→j) The duration of the green light for the corresponding traffic flow is t T(i→j) The number of vehicles that can leave the downstream intersection within 1 hour is calculated by equation (28). is the corresponding traffic flow f S(i→j) During the green light duration t T(i→j)The average arrival flow rate in the area, S is taken as 1, 2, 4, satisfying formula (29), and the green light duration t T(i→j) Satisfy formula (30);

[0076]

[0077]

[0078]

[0079] Where, T L,j Coordinate traffic flow for the goal f O(i→j) Or target coordinated update of traffic flow f H(i→j) The last vehicle ideally passes downstream intersection I j Ideal passing time of the stop line, calculate the clearing time t C(i→j) Satisfying formula (31):

[0080]

[0081] If F A1(i→j) +F A2(i→j) +F A4(i→j) = 0, then there is no non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) Or non-target coordinated merging of traffic flows f 4(i→j) The release phase is followed by the target coordination phase, and the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t C(i→j) Equal to the maximum clearing time of all non-target coordinated traffic flows End the optimization process;

[0082] S403, calculate the target coordinated vehicle flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t in case of parking and waiting C(i→j) ; If there is a target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Stop and wait, then the goal is to coordinate traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t C(i→j) Satisfy formula (32); coordinate the phase green light end time T D,j Until the ideal passing time of the last target coordinated vehicle T L,j The ideal release time between W(i→j) Satisfy formula (33);

[0083]

[0084]

[0085]

[0086] Where, For traffic flow f S(i→j) At the ideal release time t W(i→j) The average arrival flow rate within.

[0087] As a preferred technical solution, step S403 further includes the following:

[0088] Calculate the ideal release time t W(i→j) Satisfying formula (35), clearing time t C(i→j) When formula (36) is satisfied, the optimization process ends;

[0089]

[0090] Compared with the existing technology, the beneficial effects of the present invention are as follows:

[0091] 1) Based on the analysis of the green wave coordinated control demand characteristics, the present invention provides the definition and calculation method of the relevant parameters of the sampled trajectory vehicle coordinated control, and provides a research idea for the arrival flow rate estimation.

[0092] 2) The present invention can estimate the arrival flow rates of various coordinated vehicle flows based on sampled trajectory data, providing a research method for vehicle-level arrival flow rate estimation for green wave coordination needs.

[0093] 3) The present invention calculates the number of vehicles in the queue by combining the arrival flow rates of the traffic flows in each direction, provides a method for calculating the queue clearing time of the target coordinated vehicles, and provides a technical basis for the green wave coordination design of the head of the queue. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 It is a flow chart of a method for calculating the green wave coordinated clearing time based on sampled trajectory data;

[0095] Figure 2 It is a schematic diagram of the arrival flow rate within the cycle proposed by the present invention;

[0096] Figure 3 is a schematic diagram of any sampled vehicle in the set A proposed in the present invention;

[0097] Figure 4 is a schematic diagram of any adjacent sampled vehicles in the set B proposed in the present invention;

[0098] Figure 5 is a schematic diagram of any adjacent sampled vehicles in the set C proposed by the present invention;

[0099] Figure 6It is a schematic diagram of the queuing situation of traffic flows in various directions proposed by the present invention;

[0100] Figure 7 This is a schematic diagram of the classification of vehicle arrival conditions proposed in the present invention;

[0101] Figure 8 This is a schematic diagram of the non-target coordination phase followed by the target coordination phase proposed by the present invention;

[0102] Figure 9 This is a schematic diagram of the non-target coordination phase not following the target coordination phase proposed by the present invention;

[0103] Figure 10 This is a schematic diagram of a target coordinated vehicle parking waiting situation proposed in the present invention;

[0104] Figure 11 It is a schematic diagram of the arrival flow rate within the cycle proposed by the present invention;

[0105] Figure 12 This embodiment collects non-target merging traffic flow f within a certain period 4(1→2) Trajectory time diagram;

[0106] Figure 13 The target coordinated update traffic flow f in this embodiment H(1→2) Arrival flow rate diagram;

[0107] Figure 14 is the non-target coordinated merged traffic flow f in this embodiment 4(1→2) Arrival flow rate diagram;

[0108] Figure 15 is the non-target coordinated traffic flow f in this embodiment 3(1→2) Arrival flow rate diagram. DETAILED DESCRIPTION

[0109] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0110] The intersection of Fenjiang Middle Road and Tongji Road in Chancheng District, Foshan was selected as the upstream intersection I1 (I i , i=1), the intersection of Tiyu Road and Tongji Road is the downstream intersection I2 (I j ,j=2), the intersection structure and signal control scheme are as follows Figure 11 As shown, in this embodiment, the straight-ahead direction from west to east is taken as the target coordination direction.

[0111] like Figure 1 As shown, this embodiment provides a method for calculating green wave coordinated clearing time based on sampled trajectory data, including the following steps:

[0112] Step S1: For the coordinated path P from the upstream intersection I1 to the downstream intersection I2 (1→2), the classification definition and its related traffic flow f S(1→2) , detect the parking state S of the sample vehicle n at the downstream intersection I2 (n,2) and parking time T S(n,2) , calculate its actual passing time T P(n,2) and ideal passing time T E(n,2) , combined with the sampled vehicle trajectories to calculate the number of arriving vehicles, the specific steps are as follows:

[0113] S101, for the coordinated path P from the upstream intersection I1 to the downstream intersection I2 (1→2) , the classification definition and its related traffic flow f S(1→2) .

[0114] When the upstream intersection I1 to the downstream intersection I2 coordinates the path P (1→2) The goal of coordinating traffic flow f O(1→2) When designing the green wave coordination of the first type of team, the target coordinated traffic flow f in this embodiment is O(1→2) , non-target coordinated traffic flow f 1(1→2) 、f 2(1→2) and f 3(1→2) The definition and selection of are shown in Table 1, where f (1,x→2,y) The traffic flow enters from the upstream intersection I1 in the x direction and leaves the downstream intersection I2 in the y direction. When x is 1, 2, and 3, it represents the traffic flow turning left, going straight, and turning right from the upstream intersection I1 to enter the downstream intersection I2. When y is 1, 2, and 3, it represents the traffic flow turning left, going straight, and turning right at the current entrance to leave the downstream intersection I2. The traffic flow f (1,x→2,D) The D direction is the target coordinated traffic flow f O(1→2) At the downstream intersection I2, the departure direction of the traffic flow f (1,x→2,D′) Where D′ is the target coordinated traffic flow f O(1→2) Define a Boolean variable F for all departure directions of the shared lane at the downstream intersection I2. R(1→2) To determine whether the right-turn merging traffic at the upstream intersection I1 is controlled, define the Boolean variable F C(1→2) To determine the downstream intersection I j Coordinate whether the route entrance is a shared lane.

[0115] Table 1 Definitions of target coordinated traffic flow and non-target merging traffic flow in this case

[0116]

[0117] Target coordinated traffic flow f O(1→2) The arrival flow rate is denoted as λ O(1→2) , non-target coordinated traffic flow f 1(1→2) The arrival flow rate is denoted as λ 1(1→2)In this embodiment, the right-turn merging traffic flow at the upstream intersection I1 is not controlled by a signal, so it is defined as a non-target coordinated traffic flow f 3(1→2) The arrival flow rate λ 3(1→2) Perform calculations.

[0118] According to the periodic signal phase setting, the situations in which traffic flows merge into the downstream intersection within the period can be divided into two categories: one is that the signal phase setting of the upstream intersection I1 prevents traffic flows from mixing and entering the downstream intersection I2; the other is that the signal phase setting of the upstream intersection I1 prevents traffic flows from mixing and entering the downstream intersection I2.

[0119] In this embodiment, the signal phase setting of the upstream intersection I1 causes mixed traffic to enter the downstream intersection I2. The traffic released during the left-turn phase release period at the north entrance of the upstream intersection I1 is non-target coordinated traffic. 1(1→2) and f 3(1→2) , and record the merger as non-target coordinated merged traffic flow f 4(1→2) , whose arrival flow rate is λ 4(1→2) , the released traffic flow during the straight phase release period of the upstream intersection I1 west entrance has both target coordinated traffic flow f O(1→2) There is also a non-target coordinated traffic flow f 3(1→2) , merge them into the target coordinated update traffic flow f H(1→2) , whose arrival flow rate is λ H(1→2) ,like Figure 2 As shown in parts (a) and (b) of the figure.

[0120] According to the above traffic flow division results, the coordinated path P (1→2) The relevant traffic flows in each direction are defined as f S(1→2) , S can be O, H, 1, 2, 3, 4, representing the target coordinated traffic flow f O(1→2) , target coordinated update traffic flow f H(1→2) , non-target coordinated traffic flow f 1(1→2) , non-target coordinated traffic flow f 2(1→2) , non-target coordinated traffic flow f 3(1→2) and non-target coordinated merging of traffic flows f 4(1→2) .

[0121] S102, detecting the parking state S of the sampled vehicle n at the downstream intersection I2 (n,2) and parking time T S(n ,2 ) , calculate its actual passing time T P(n,2) and ideal passing time T E(n,2) If the sampled vehicle n has been stopped at the downstream intersection I2, the stopped state S (n,2) Take 1, otherwise take 0. If the parking state S of the sampled vehicle n(n,2) =0, based on the detection time of the trajectory points before and after the stop line of intersection I2 and the distance between the sampled vehicle n and the stop line, the actual passing time T of the sampled vehicle n passing the stop line of the entrance lane of the coordinated path of intersection I2 is calculated by interpolation method. P(n,2) If the parking state S of the sampled vehicle n (n,2) =1, detecting the first parking time T of the sample vehicle n at the entrance of the coordinated path of intersection I2 S(n,2) , calculate the ideal passing time T of the sample vehicle n passing the ideal intersection I2 coordinated path entrance lane stop line E(n,2) , as shown in formula (1).

[0122]

[0123] Where D (n,2) is the distance between the sampling vehicle n and the stop line when it stops for the first time at the entrance of the coordinated path of intersection I2, It is the average ideal driving speed of the vehicle on the current road section.

[0124] Table 2 Non-target merging traffic flow f in a certain period 4(1→2) data

[0125]

[0126] The non-target coordinated merged traffic flow f collected within a certain period 4(1→2) For example, the parking state S before the downstream intersection I2 stop line (n,1→2) , parking time T S(n,2) , actual passing time T P(n,2) and the ideal passing time T E(n,2) As shown in Table 2.

[0127] S103, according to the flow of traffic f S(1→2) Arrival situation Calculate the number of arriving vehicles. Discretize the continuous arrival time within the signal period with k = 1 second as the time interval. m The number of vehicles that ideally pass the intersection stop line during the time period is counted, and the vehicle arrival ratio P(t m ) as shown in formula (2).

[0128]

[0129] Where N is the total number of vehicles whose trajectories can be collected, m is the time interval number after discrete processing, and F{T E(n,2) =t m} is the ideal passing time T for judging the collection of any sampled vehicle n E(n,2) Is it in t mThe indicator variable within a period is calculated as shown in formula (3).

[0130]

[0131] Traverse the sampled trajectory data of the S-flow vehicle flow, and sequentially include the vehicle trajectory into the data set of different situations U. S can take O, H, 1, 2, 3, and 4, which represent the target coordinated vehicle flow f, respectively. O(1→2) , target coordinated update traffic flow f H(1→2) , non-target coordinated traffic flow f 1(1→2) 、f 2(1→2) 、f 3(1→2) and non-target coordinated merging of traffic flows f 4(1→2) , case U can be 1, 2, or 3. Case 1 corresponds to the first sampled vehicle n in all S-flow traffic within the signal period. A The driving trajectory is the parking trajectory. Case 2 corresponds to the two adjacent sampling vehicles in the S flow within the signal period. (front car) and n B The driving trajectories of the following vehicles are all parking trajectories. Case 3 corresponds to the two adjacent sampled vehicles in the S-flow traffic flow within the signal period. (front car) and n C The driving trajectories of the following vehicles are respectively parking trajectories and non-parking trajectories. Other sampling trajectory collection situations cannot provide effective information for traffic arrival flow rate estimation and are therefore not considered.

[0132] For the sampled trajectory data of the S-flow traffic, all trajectory data that meet the condition 1 are recorded as set A, and any sampled vehicle n in set A is recorded as A The estimated number of arriving vehicles is recorded as All trajectory data that meet condition 2 are recorded as set B, and any adjacent sampled vehicle in set B is With n B The estimated number of arriving vehicles is recorded as All trajectory data that meet condition 3 are recorded as set C, and any adjacent sampled vehicle in set C is With n C The estimated number of arriving vehicles is recorded as

[0133] a) For any sampled vehicle n in set A A , estimate the red light turn-on time of the coordinated phase The ideal passing time with the current vehicle trajectory The number of vehicles arriving at the downstream intersection I2 coordinated path corresponding to the traffic flow As shown in formula (4) and formula (5).

[0134]

[0135]

[0136]

[0137] In the formula, [] is the rounding operator, To coordinate the phase green light on time To sample vehicle n A Actual passing time The length of the green light between and are the sampled vehicles n A When passing through the downstream intersection I2, the red and green lights corresponding to the coordinated phase are turned on, h s =2(s) is the saturation headway, Poisson is the number of arriving vehicles Obeying Poisson distribution, λ S(1→2) is the arrival flow rate of traffic to S, The number of vehicles arriving The corresponding time interval, such as Figure 3 shown.

[0138] b) For any adjacent sampled vehicles in set B With n B ,like Figure 4 As shown, the estimated sample vehicles Ideal passing time With sampled vehicles n B Ideal passing time Number of vehicles arriving between The calculation satisfies formula (7).

[0139]

[0140]

[0141]

[0142] Where, For sampling vehicles Actual passing time With sampled vehicles n B Actual passing time The length of the green light between The number of vehicles arriving The corresponding time interval.

[0143] c) For any adjacent sampled vehicles in set C With n C ,like Figure 5 As shown, the estimated sample vehicles Ideal passing time With sampled vehicles n C Actual passing time Number of vehicles arriving between The calculation satisfies formula (10).

[0144]

[0145]

[0146]

[0147] Where, For sampling vehicles Actual passing time With sampled vehicles n C Actual passing time The green light duration between S3 ( nC,1→2) The number of vehicles arriving The corresponding time interval.

[0148] The non-target coordinated merged traffic flow f collected within a certain period 4(1→2) For example, the trajectory time diagram is as follows Figure 12 As shown, the valid trajectory data in set A is shown in Table 3, and the trajectory data in set B is shown in Table 4.

[0149] Table 3 Valid trajectory data in set A

[0150]

[0151]

[0152] Table 4 Trajectory data in set B

[0153]

[0154] Step S2: Construct the traffic flow f in each direction S(1→2) The arrival likelihood function estimates the traffic flow f in each direction S(1→2) The arrival flow rate λ S(1→2) , the specific steps are as follows:

[0155] S201, construct the traffic flow f in each direction S(1→2) The arrival likelihood function of .

[0156] According to the three sets obtained in step S103, the arrival likelihood function for the S-flow traffic is constructed as shown in formula (13), where h is any adjacent sampled vehicle in set C. With n C The number of vehicles that may exist between the two, the maximum value is the number of arriving vehicles A total of a sampled vehicle trajectories were collected in set A, a total of b pairs of adjacent sampled vehicle trajectories were collected in set B, and a total of c pairs of adjacent sampled vehicle trajectories were collected in set C.

[0157]

[0158] S202: Estimating the traffic flow f in each direction S(1→2) The arrival flow rate λ S(1→2) Based on Bayesian inference, the exponential distribution is taken as the arrival flow rate λ S(1→2) The prior distribution of the exponential distribution parameter β S(1→2) is a known empirical value, and the arrival flow rate λ S(1→2) is a positive value, then the arrival flow rate λ S(1→2) The prior distribution function and posterior probability expressions of are shown in Equations (14) and (15).

[0159]

[0160]

[0161] The maximum a posteriori estimation method is used to estimate the parameters, using the parameter variable value at the maximum posterior probability as the parameter point estimate. The denominator of the posterior probability, P(D), is a constant value that does not affect the maximum value point of the posterior probability function and can be ignored. The posterior probability function is logarithmized and simplified to obtain Equation (16).

[0162]

[0163] for The estimated value of is calculated by equation (17), where l represents the number of loop iterations and g represents any adjacent sampled vehicle in the set C. With n C The maximum number of vehicles that may exist between

[0164]

[0165] The flow S is directed to the vehicle flow reaching the flow rate λ S(1→2) The average value of the arrival flow rate is recorded as the average arrival flow rate The maximum a posteriori estimation method is combined with formula (17) to iterate the calculation until convergence, and the result satisfies formula (18).

[0166]

[0167] Furthermore, the converged average arrival flow rate Multiplying by the vehicle arrival ratio, we can get the traffic flow f in each direction S(1→2) Arrival flow rate λ at different time periodsS(1→2) The expression satisfies formula (19).

[0168]

[0169] In this embodiment, the target coordinated update traffic flow f is calculated H(1→2) Arrival flow rate, non-target coordinated merging traffic flow f 4(1→2) Arrival flow rate, non-target coordinated traffic flow f 3(1→2) Arrival flow rate Figure 13 、 Figure 14 、 Figure 15 shown.

[0170] Step S3: Calculate the maximum clearing time of all non-target coordinated traffic flows Specifically:

[0171] S301. Calculate the maximum clearing time of all non-target coordinated traffic flows The time for clearing all non-target coordinated traffic flows is defined as t CT ( 1→2) , is the maximum clearing time of non-target coordinated traffic flow, and the calculation satisfies formula (20). S(1→2) Corresponding to the traffic flow f in each direction S(1→2) The time it takes for queued vehicles to be cleared, The clearing time t S(1→2) The maximum clearing time to reach the maximum value, Q S(1→2) Corresponding to the traffic flow f in each direction S(1→2) The number of vehicles in the queue, is the number of vehicles in the queue Q S(1→2) The maximum number of vehicles in the queue that reaches the maximum value, S (1→2) Coordinate traffic flow for the goal f O(1→2) Or target coordinated update of traffic flow f H(1→2) The sum of the saturated flows of all lanes in the corresponding coordinated path, the Boolean variable F S(1→2) Used to determine whether there is traffic flow f in the traffic flow division result S(1→2) , there is traffic flow f S(1→2) Then F S(1→2) Take 1, otherwise take 0, where S can take O, H, 1, 2, 3, 4. In this embodiment, there is F O(1→2) =0, F H(1→2) =1, F 1(1→2) =0, F 2(1→2) =0, F 3(1→2) =1, F 4(1→2) =1.

[0172]

[0173]

[0174]

[0175]

[0176]

[0177] Where, is the average starting loss time of the vehicles in the queue, such as Figure 6 As shown, T 1,2 With T 2,2 For non-target coordinated traffic flow f 1(1→2) With f 2(1→2) The ideal time when the first vehicle passes through the downstream intersection I2 stop line is T F,2 Coordinate traffic flow for the goal f O(1→2) Or target coordinated update of traffic flow f H(1→2) The ideal passing time of the first car passing through the downstream intersection I2 stop line is T 4,2 For non-target coordinated merging of traffic flows f 4(1→2) The ideal time when the first vehicle passes through the downstream intersection I2 stop line is T O,2 With T E,2 are the start and end time points of the cycle of the downstream intersection I2 target coordination phase, T D,2 The green light end time of the coordinated phase for the downstream intersection I2.

[0178] According to this embodiment, after calculation

[0179] Step S4: Coordinate the phase green light duration and maximum clearing time according to the upstream and downstream intersections Determine the target coordinated vehicle at the downstream intersection I j The passing situation of the target coordinated traffic flow f is calculated O(1→2) Or target coordinated update of traffic flow f H(1→2) Clearing time t C(1→2) , specifically:

[0180] S401, based on the green light duration of the upstream and downstream intersections and the maximum clearing time of all non-target coordinated traffic flows Determine the target coordinated traffic flow f O(1→2) Or target coordinated update of traffic flow f H(1→2) The passing situation at the downstream intersection I2.

[0181] Green light duration t of I2 coordinated phase G(2→3) Coordinate the maximum clearing time of traffic with all non-target vehicles The green light duration t of the coordinated phase with the upstream intersection I1 G(1→2)If the sum of Then the target coordinated traffic flow f O(1→2) Or target coordinated update of traffic flow f H(1→2) All vehicles in the queue are able to pass through the downstream intersection in the current cycle, and all vehicles in the queue are composed of non-target coordinated traffic. Then the target coordinated traffic flow f O(1→2) Or target coordinated update of traffic flow f H(1→2) It is not possible to pass all downstream intersections in the current cycle, such as Figure 7 As shown, the queued vehicles consist of non-target coordinated traffic flow and target coordinated traffic flow f O(1→2) Or target coordinated update of traffic flow f H(1→2) composition.

[0182] After calculation, in this embodiment, the green light duration of the upstream intersection coordination phase is t G(1→2) =32s, downstream intersection coordination phase green light duration t G(2→3) = 45s, so Target coordinated update of traffic flow f H(1→2) If all traffic can pass through the downstream intersection in the current cycle and all queued vehicles are composed of non-target coordinated traffic, go to step S402.

[0183] S402, calculate the target coordinated traffic flow f O(1→2) Or target coordinated update of traffic flow f H(1→2) Clearing time t in case of parking and waiting C(1→2) . Define t C(1→2) The target coordinated traffic flow f from the upstream intersection I1 to the downstream intersection I2 O(1→2) Or target coordinated update of traffic flow f H(1→2) The time required to clear the queued vehicles when the head-of-line green wave coordination design is adopted. If there is no target coordinated vehicle waiting at the arrival of the vehicle, then t C(1→2) =t CT(1→2) . Define the Boolean variable F AS(1→2) To determine the traffic flow f S(1→2) Whether the release phase follows the target coordination phase, if so, the Boolean variable F AS(i→j) Takes 1, otherwise takes 0, where S can take 1, 2, or 4. F AS(1→2) Satisfies formula (25).

[0184] F A1(1→2) +F A2(1→2) +F A4(1→2) ≤1 (25)

[0185] If F A1(1→2) +F A2(1→2) +F A4(1→2) =1, then there is a non-target coordinated traffic flow f 1(1→2) 、f 2(1→2)Or non-target coordinated merging of traffic flows f 4(1→2) The release phase follows the target coordination phase, and some non-target coordinated vehicles can pass through the downstream intersection I2 in the current cycle. A1(i→j) =0, F A2(i→j) =0, F A4(i→j) =1, non-target coordinated merging of traffic flows f 4(1→2) The release phase follows the target coordination phase. The green light duration of the traffic flow that is allowed to pass through the downstream intersection in the current cycle is defined as t T(1→2) , satisfying formula (26). At this time, the clearing time t C(1→2) Satisfies formula (27).

[0186] t T(1→2) =t G(2→3) -t G(1→2) -t C(1→2) (26)

[0187]

[0188] Where Q T(1→2) The duration of the green light for the corresponding traffic flow is t T(1→2) The number of vehicles that can leave the downstream intersection within 1 hour is calculated by equation (28). is the corresponding traffic flow f S(1→2) During the green light duration t T(1→2) The average arrival flow rate in the area, S can be taken as 1, 2, 4, satisfying formula (29), and the green light duration t T(1→2) Satisfies formula (30).

[0189]

[0190]

[0191]

[0192] Where, T L,2 Coordinate traffic flow for the goal f O(1→2) Or target coordinated update of traffic flow f H(1→2) The ideal passing time of the last vehicle passing through the downstream intersection I2 stop line, further, the clearing time t can be calculated C(1→2) Satisfying formula (31), such as Figure 8 shown.

[0193]

[0194] If F A1(1→2) +F A2(1→2) +F A4(1→2) = 0, then there is no non-target coordinated traffic flow f1(1→2) 、f 2(1→2) Or non-target coordinated merging of traffic flows f 4(1→2) The release phase is followed by the target coordination phase, such as Figure 9 As shown, the target coordinated traffic flow f O(1→2) Or target coordinated update of traffic flow f H(1→2) Clearing time t C(1→2) Equal to the maximum clearing time of all non-target coordinated traffic flows

[0195] After calculation, the green light duration for passage is t T(1→2) =5.5437s, clearing time t C(i→j) =7.4562s, i.e., the target coordinated update of traffic flow f H(1→2) The clearing time when the team-head green wave coordinated design method is adopted is 7.4562 seconds, ending the optimization process.

[0196] S403, calculate the target coordinated vehicle flow f O(1→2) Or target coordinated update of traffic flow f H(1→2) Clearing time t in case of parking and waiting C(1→2) .like Figure 10 As shown, if there is a target coordinated traffic flow f O(1→2) Or target coordinated update of traffic flow f H(1→2) Stop and wait, then the goal is to coordinate traffic flow f O(1→2) Or target coordinated update of traffic flow f H(1→2) Clearing time t C(1→2) Satisfy formula (32). Set the coordination phase green light end time T D,2 Until the ideal passing time of the last target coordinated vehicle T L,2 The ideal release time between W(1→2) Satisfies formula (33).

[0197]

[0198]

[0199]

[0200] Where, For traffic flow f S(1→2) At the ideal release time t W(1→2) The average arrival flow rate within . Further, calculate the ideal release time t W(1→2) Satisfying formula (35), clearing time t C(1→2) When equation (36) is satisfied, the optimization process ends.

[0201]

[0202]

[0203] Since there is no target coordinated traffic flow f in this embodiment O(1→2) Or target coordinated update of traffic flow f H(1→2) In the case of parking and waiting, this step does not need to be calculated.

[0204] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for calculating green wave coordinated clearing time based on sampled trajectory data, characterized in that: The following steps are involved: S1, for the upstream intersection I i To downstream intersection I j The coordination path P (i→j) , the classification definition and its related traffic flow f S(i→j) , detect sampling vehicle n at downstream intersection I j Parking status S (n,j) and parking time T S(n,j) , calculate its actual passing time T P(n,j) and ideal passing time T E(n,j) , calculate the number of arriving vehicles in combination with the sampled vehicle trajectories; the step S1 is specifically as follows: S101, for the upstream intersection I i To downstream intersection I j The coordination path P (i→j) , the classification definition and its related traffic flow f S(i→j) ; When the upstream intersection I i To downstream intersection I j Coordination path P (i→j) The goal of coordinating traffic flow f O(i→j) When designing the green wave coordination of the first type of team, the target coordinated traffic flow f under different circumstances O(i→j) , non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) and f 3(i→j) The definition and selection of are shown in Table 1, where f (i,x→j,y) From upstream intersection I i The x-flow direction enters and downstream intersection I j The y-flow direction of the vehicle leaving, when x is 1, 2, 3, it represents the vehicle flow from the upstream intersection I i Turn left, go straight, turn right and go to the downstream intersection I j When y is 1, 2, or 3, it means that the traffic flow turns left, goes straight, or turns right to leave the downstream intersection I at the current entrance. j , traffic flow (i,x→j,D) The D direction is the target coordinated traffic flow f O(i→j) At downstream intersection I j The departure direction of the dedicated lane, the traffic flow f (i,x→j,D′) The D′ direction is the target coordinated vehicle flow f O(i→j) At downstream intersection I j All departure directions of the shared lane, define a Boolean variable F R(i→j) To determine the upstream intersection I i Define the Boolean variable F to determine whether the right-turn merging traffic is controlled. C(i→j) To determine the downstream intersection I j Coordinate whether the entrance lane of the route is a shared lane; Table 1 Definitions of target coordinated traffic flow and non-target coordinated traffic flow in different situations Target coordinated traffic flow f O(i→j) The arrival flow rate is denoted as λ O(i→j) , non-target coordinated traffic flow f 1(i→j) and f 2(i→j) The arrival flow rate is denoted as λ 1(i→j) and λ 2(i→j) ; If the upstream intersection I i The right-turn merging traffic flow is not controlled by signals, so it is defined as non-target coordinated traffic flow f 3(i→j) The arrival flow rate λ 3(i→j) Perform calculations; According to the periodic signal phase setting, the traffic flow in the period is merged into the downstream intersection I j There are two situations: one is the upstream intersection I i The signal phase is set so that there is no mixing between vehicles entering the downstream intersection I j The second is the upstream intersection I i The signal phase setting allows for mixed traffic flow into the downstream intersection I j phenomenon; When the upstream intersection I i The signal phase is set so that there is no mixing between vehicles entering the downstream intersection I j When the phenomenon occurs, estimate the target coordinated traffic flow f O(i→j) Coordinate traffic flow with non-target f 1(i→j) 、f 2(i→j) Vehicle arrival situation; when the upstream intersection I i The signal phase setting makes the traffic flow mixed into the downstream intersection I j If the traffic flow released in the same period is non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) or f 3(i→j) Two of them are merged and recorded as non-target coordinated merged traffic flow f 4(i→j) , whose arrival flow rate is λ 4(i→j) , if the released traffic flow exists in the same period and there is a target coordinated traffic flow f O(i→j) There are also other non-target coordinated traffic flows f 1(i→j) 、f 2(i→j) or f 3(i→j) , merge them into the target coordinated update traffic flow f H(i→j) , whose arrival flow rate is λ H(i→j) ; According to the above traffic flow division results, the coordinated path P (i→j) The relevant traffic flows in each direction are defined as f S(i→j) , S takes O, H, 1, 2, 3, 4, representing the target coordinated traffic flow f O(i→j) , target coordinated update traffic flow f H(i→j) , non-target coordinated traffic flow f 1(i→j) , non-target coordinated traffic flow f 2(i→j) , non-target coordinated traffic flow f 3(i→j) and non-target coordinated merging of traffic flows f 4(i→j) ; S102, detecting sampling vehicle n at downstream intersection I j Parking status S (n,j) and parking time T S(n,j) , calculate its actual passing time T P(n,j) and ideal passing time T E(n,j) ; If the sampling vehicle n is at the downstream intersection I j There is a parking state, parking state S (n,j) Take 1, otherwise take 0; if the parking state S of the sampled vehicle n (n,j) is 0, based on the sampling vehicle n at intersection I j The detection time of the trajectory points before and after the stop line and the distance between them and the stop line are calculated by interpolation method to calculate the actual time when the sampled vehicle n passes through the intersection I j The actual passing time T of the stop line at the entrance of the coordinated path P(n,j) ; If the parking state S of the sampled vehicle n (n,j) is 1, detecting the sampling vehicle n at intersection I j The first stop time T of the coordinated path entrance S(n,j) , calculate the ideal number of sampled vehicles n passing through intersection I j The ideal passing time T of the stop line at the entrance of the coordinated path E(n,j) , as shown in formula (1): Where D (n,j) is the sampling vehicle n at intersection I j The distance between the coordinated path entrance lane and the stop line when the first stop is made, is the average ideal driving speed of the vehicle on the current road section; S103, according to the flow of traffic f S(i→j) Arrival situation: calculate the number of arriving vehicles; discretize the continuous arrival time within the signal period with a time interval of k seconds. m The number of vehicles that ideally pass the intersection stop line during the time period is counted, and the vehicle arrival ratio P(t m ) is shown in formula (2): Where N is the total number of vehicles whose trajectories can be collected, m is the time interval number after discrete processing, and F{T E(n,j) =t m } is the ideal passing time T for judging the collection of any sampled vehicle n E(n,j) Is it in t m The indicator variable within a period is calculated as shown in formula (3): Traverse the sampled trajectory data of the S-flow vehicle flow, and sequentially include the vehicle trajectory into the data set of different situations U. S takes O, H, 1, 2, 3, and 4, which represent the target coordinated vehicle flow f respectively. O(i→j) , target coordinated update traffic flow f H(i→j) , non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) 、f 3(i→j) and non-target coordinated merging of traffic flows f 4(i→j) , case U takes 1, 2, 3, case 1 corresponds to the first sampling vehicle n in all S-flow traffic within the signal period A The driving trajectory is the parking trajectory. Case 2 corresponds to the preceding vehicle of two adjacent sampled vehicles in the S-flow traffic flow within the signal period. With the following car n B The driving trajectories are all parking trajectories. Case 3 corresponds to the preceding vehicle of two adjacent sampled vehicles in the S-flow traffic flow within the signal period. With the following car n C The driving trajectories are divided into parking trajectories and non-parking trajectories; other sampling trajectory collection situations cannot provide effective information for traffic arrival flow rate estimation and are therefore not considered; For the sampled trajectory data of the S-flow traffic, all trajectory data that meet the condition 1 are recorded as set A, and any sampled vehicle n in set A is recorded as A The estimated number of arriving vehicles is recorded as All trajectory data that meet condition 2 are recorded as set B, and any adjacent sampled vehicle in set B is With n B The estimated number of arriving vehicles is recorded as All trajectory data that meet condition 3 are recorded as set C, and any adjacent sampled vehicle in set C is With n C The estimated number of arriving vehicles is recorded as S2. Construct the traffic flow f in each direction S(i→j) The arrival likelihood function estimates the traffic flow f in each direction S(i→j) The arrival flow rate λ S(i→j) ; S3. Calculate the maximum clearing time of all non-target coordinated traffic flows S4. Coordinate the phase green light duration and maximum clearing time according to the upstream and downstream intersections Determine the target coordinated vehicle at the downstream intersection I j The passing situation of the target coordinated traffic flow f is calculated O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t C(i→j) .

2. The method for calculating green wave coordinated clearing time based on sampled trajectory data according to claim 1, characterized in that: In step S103, for any sampled vehicle n in set A, A , estimate the red light turn-on time of the coordinated phase The ideal passing time with the current vehicle trajectory Downstream Intersection I j The number of arriving vehicles corresponding to the coordinated path As shown in formula (4) and formula (5): In the formula, [] is the rounding operator, To coordinate the phase green light on time To sample vehicle n A Actual passing time The length of the green light between and are the sampled vehicles n A Pass downstream intersection I j The time corresponding to the red and green lights turning on in the coordinated phase, h s is the saturation headway, Poisson represents the number of arriving vehicles Obeying Poisson distribution, λ S(i→j) is the arrival flow rate of traffic to S, The number of vehicles arriving The corresponding time interval.

3. The method for calculating green wave coordinated clearing time based on sampled trajectory data according to claim 2, characterized in that: In step S103, for any adjacent sampled vehicles in set B, With n B , estimating the sampled vehicles Ideal passing time With sampled vehicles n B Ideal passing time Number of vehicles arriving between The calculation satisfies formula (7): Where, For sampling vehicles Actual passing time With sampled vehicles n B Actual passing time The length of the green light between The number of vehicles arriving The corresponding time interval.

4. The method for calculating green wave coordinated clearing time based on sampled trajectory data according to claim 3, characterized in that: In step S103, for any adjacent sampled vehicles in set C, With n C , estimating the sampled vehicles Ideal passing time With sampled vehicles n C Actual passing time Number of vehicles arriving between The calculation satisfies formula (10): Where, For sampling vehicles Actual passing time With sampled vehicles n C Actual passing time The length of the green light between The number of vehicles arriving The corresponding time interval.

5. The method for calculating green wave coordinated clearing time based on sampled trajectory data according to claim 4, characterized in that: The step S2 is specifically as follows: S201, construct the traffic flow f in each direction S(i→j) The arrival likelihood function of According to the three sets obtained in step S103, the arrival likelihood function for the S-flow traffic is constructed as shown in formula (13), where h is any adjacent sampled vehicle in set C. With n C The number of vehicles that may exist between the two, the maximum value is the number of arriving vehicles There are a total of a sampled vehicle trajectories collected in set A, b pairs of adjacent sampled vehicle trajectories collected in set B, and c pairs of adjacent sampled vehicle trajectories collected in set C; S202: Estimating the traffic flow f in each direction S(i→j) The arrival flow rate λ S(i→j) Based on Bayesian inference, we take the exponential distribution as the arrival flow rate λ S(i→j) The prior distribution of the exponential distribution parameter β S(i→j) is a known empirical value, and the arrival flow rate λ S(i→j) is a positive value, then the arrival flow rate λ S(i→j) The prior distribution function and posterior probability expressions of are shown in formula (14) and formula (15): The maximum a posteriori estimation method is used to estimate the parameters, using the parameter variable value when the posterior probability is the maximum as the point estimate of the parameter. The denominator P(D) of the posterior probability is a constant value and will not affect the maximum value point of the posterior probability function, so it is ignored. The posterior probability function is logarithmically processed and simplified to obtain formula (16): for The estimated value of is calculated by equation (17), where l represents the number of loop iterations and g represents any adjacent sampled vehicle in the set C. With n C The maximum number of vehicles that may exist between The flow S is directed to the vehicle flow reaching the flow rate λ S(i→j) The average value of the arrival flow rate is recorded as the average arrival flow rate The maximum a posteriori estimation method is combined with formula (17) and iterative calculation is performed until convergence, and the result satisfies formula (18):

6. The method for calculating green wave coordinated clearing time based on sampled trajectory data according to claim 5, characterized in that: The step S202 further includes the following: The average arrival flow rate will converge Multiply it by the vehicle arrival ratio to obtain the traffic flow f in each direction S(i→j) Arrival flow rate λ at different time periods S(i→j) The expression satisfies formula (19):

7. The method for calculating green wave coordinated clearing time based on sampled trajectory data according to claim 6, characterized in that: The step S3 is specifically as follows: S301. Calculate the maximum clearing time of all non-target coordinated traffic flows The time for clearing all non-target coordinated traffic flows is defined as t CT(i→j) , is the maximum clearing time of all non-target coordinated traffic flows, calculated to satisfy formula (20); t S(i→j) Corresponding to the traffic flow f in each direction S(i→j) The time it takes for queued vehicles to be cleared, The clearing time t S(i→j) The maximum clearing time to reach the maximum value, Q S(i→j) Corresponding to the traffic flow f in each direction S(i→j) The number of vehicles in the queue, is the number of vehicles in the queue Q S(i→j) The maximum number of vehicles in the queue that reaches the maximum value, S (i→j) Coordinate traffic flow for the goal f O(i→j) Or target coordinated update of traffic flow f H(i→j) The sum of the saturated flows of all lanes in the corresponding coordinated path, the Boolean variable F S(i→j) Used to determine whether there is traffic flow f in the traffic flow division result S(i→j) , there is traffic flow f S(i→j) Then F S(i→j) Takes 1, otherwise takes 0, where S takes O, H, 1, 2, 3, 4; Where, is the average starting loss time of vehicles in the queue, T 1,j With T 2,j For non-target coordinated traffic flow f 1(i→j) With f 2(i→j) The first vehicle ideally passes downstream intersection I j The ideal time to pass the stop line, T F,j Coordinate traffic flow for the goal f O(i→j) Or target coordinated update of traffic flow f H(i→j) The first vehicle ideally passes downstream intersection I j The ideal time to pass the stop line, T 4,j For non-target coordinated merging of traffic flows f 4(i→j) The first vehicle ideally passes downstream intersection I j The ideal time to pass the stop line, T O,j With T E,j Downstream intersection I j The start and end time points of the target coordination phase cycle, T D,j Downstream intersection I j The green light end time of the coordination phase.

8. The method for calculating green wave coordinated clearing time based on sampled trajectory data according to claim 7, characterized in that: The step S4 is specifically as follows: S401, based on the green light duration of the upstream and downstream intersections and the maximum clearing time of all non-target coordinated traffic flows Determine the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) At downstream intersection I j The passing situation of the downstream intersection I j Green light duration t of the coordinated phase G(j→k) Coordinate the maximum clearing time of traffic with all non-target vehicles and upstream intersection I i Green light duration t of the coordinated phase G(i→j) If the sum of Then the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) If all vehicles in the queue can pass through the downstream intersection in the current cycle and all vehicles in the queue are composed of non-target coordinated traffic, go to step S402; Then the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Unable to pass through the downstream intersection in the current cycle, the queued vehicles are divided into non-target coordinated traffic flow and target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Composition, go to step S403; S402, calculate the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t in case of parking and waiting C(i→j) ; define t C(i→j) From upstream intersection I i To downstream intersection I j The goal of coordinating traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) The time required to clear the queued vehicles when the head-of-queue green wave coordination design is adopted; If the vehicle arrives and there is no target coordinated vehicle to stop and wait, then t C(i→j) =t CT(i→j) ; Define Boolean variable F AS(i→j) To determine the traffic flow f S(i→j) Whether the release phase follows the target coordination phase, if so, the Boolean variable F AS(i→j) Take 1, otherwise take 0, where S takes 1, 2, 4; F AS(i→j) Satisfying formula (25): F A1(i→j) +F A2(i→j) +F A4(i→j) ≤1 (25) If F A1(i→j) +F A2(i→j) +F A4(i→j) =1, then there is a non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) Or non-target coordinated merging of traffic flows f 4(i→j) The release phase follows the target coordination phase, and some non-target coordinated vehicles pass through the downstream intersection I in the current cycle. j The green light duration of the traffic flow that is allowed to pass through the downstream intersection in the current cycle is defined as t T(i→j) , satisfying formula (26); at this time, the clearing time t C(i→j) Satisfy formula (27); t T(i→j) =t G(j→k) -t G(i→j) -t C(i→j) (26) Where Q T(i→j) The duration of the green light for the corresponding traffic flow is t T(i→j) The number of vehicles that can leave the downstream intersection within 1 hour is calculated by equation (28). is the corresponding traffic flow f S(i→j) During the green light duration t T(i→j) The average arrival flow rate in the area, S is taken as 1, 2, 4, satisfying formula (29), and the green light duration t T(i→j) Satisfy formula (30); Where, T L,j Coordinate traffic flow for the goal f O(i→j) Or target coordinated update of traffic flow f H(i→j) The last vehicle ideally passes downstream intersection I j Ideal passing time of the stop line, calculate the clearing time t C(i→j) Satisfying formula (31): If F A1(i→j) +F A2(i→j) +F A4(i→j) = 0, then there is no non-target coordinated traffic flow f 1(i→j) 、f 2(i→j) Or non-target coordinated merging of traffic flows f 4(i→j) The release phase is followed by the target coordination phase, and the target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t C(i→j) Equal to the maximum clearing time of all non-target coordinated traffic flows End the optimization process; S403, calculate the target coordinated vehicle flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t in case of parking and waiting C(i→j) ; If there is a target coordinated traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Stop and wait, then the goal is to coordinate traffic flow f O(i→j) Or target coordinated update of traffic flow f H(i→j) Clearing time t C(i→j) Satisfy formula (32); coordinate the phase green light end time T D,j Until the ideal passing time of the last target coordinated vehicle T L,j The ideal release time between W(i→j) Satisfy formula (33); Where, For traffic flow f S(i→j) At the ideal release time t W(i→j) The average arrival flow rate within.

9. The method for calculating green wave coordinated clearing time based on sampled trajectory data according to claim 8, characterized in that: The step S403 further includes the following contents: Calculate the ideal release time t W(i→j) Satisfying formula (35), clearing time t C(i→j) When formula (36) is satisfied, the optimization process ends;

Images