An Automatic Optimization Method for Signal Phase and Phase Sequence Based on Trajectory Conflict Index

Abstract

The present invention discloses an automatic optimization method for signal phase sequence based on trajectory conflict index. The present invention quantitatively determines the conflict degree of each flow direction from multiple aspects such as the type, quantity, angle, speed, vehicle type composition, and line of sight angle of conflict points between flow directions through high-precision trajectory data of historical vehicles at intersections. On this basis, the present invention flexibly combines each flow direction by combining the critical flow rate ratios of each flow direction to determine the basic control phase unit of the intersection. Finally, the present invention calculates the phase interval time considering the conflict of trailing vehicles between the front and rear phases, and determines the release order of the phase units with the goal of minimizing the sum of the required clearance times between all adjacent phases. Thereby, the subjectivity of manual intervention during phase sequence setting can be avoided, the labor cost can be reduced, the rationality of signal plan setting can be improved, and it is convenient for practical promotion.

Description

Technical Field

[0001] The present invention relates to a method for automatically generating signal phase parameters and phase sequences at urban intersections, and particularly to a signal optimization control method for solving intersection conflict problems, belonging to the field of intelligent transportation technology. Background Art

[0002] As the "throat" of the road network, signal intersections determine the operation efficiency of urban traffic. A signal controller can provide a safe and fair operation environment for vehicles in different directions. A reasonable control scheme can effectively improve the overall traffic operation efficiency and safety. Phase sequences and phase parameters, as the basic parameters of signal control, determine the passing order and conflict degree of all traffic participants. It is not feasible to blindly follow the traditional fixed mode for setting phase sequences and phase parameters, and more reasonable designs need to be carried out through richer detection data.

[0003] Currently, there are problems such as unreasonable settings of phase sequences at signal intersections in our city, lack of calculation and theoretical basis, etc. There are mainly the following four solutions to this problem: The first category mainly relies on practical experience to qualitatively determine the flow combinations; the second category optimizes the flow combinations and establishes an optimization model with the goal of minimizing the number of phases after combination; the third category optimizes the phase sequences and establishes a phase sequence optimization model with fixed phases with the goal of minimizing the green interval time of phases; the fourth category optimizes the flow combinations and phase sequences of phases, qualitatively establishes flow conflict constraints according to intersection channelization, obtains the basic phases with the minimum sum of critical flow ratios, and on this basis, optimizes the phase sequences with the goal of minimizing the lost time.

[0004] However, the existing methods for optimizing phase sequences consider too fixed flow combinations, and mostly avoid all conflicts mainly from the subjective safety perspective, without considering flexible settings such as straight-left mixed traffic and allowing confluence conflicts in the case of less conflict degree, and the practicality and flexibility are insufficient.

[0005] With the development of detection technologies, we can obtain vehicle trajectory-level data with higher accuracy and finer granularity. By virtue of this, we can more clearly mine the conflict relationships of traffic flows, and combine intersection safety and efficiency to obtain a more effective phase sequence setting scheme, thereby improving the utilization rate of intersection spatio-temporal resources and reducing the accident rate. Summary of the Invention

[0006] The technical problem to be solved by the present invention is: the problems of poor flexibility and low applicability existing in the current phase sequence setting methods.

[0007] To solve the above technical problem, the technical solution of the present invention is to provide a method for automatically optimizing signal phase sequences based on trajectory conflict indices, which is characterized by including the following steps:

[0008] Step S-1: Preprocessing of trajectory data:

[0009] The lane trajectory data is the single-vehicle trajectory data after intersection holographic fusion. Classify a certain number of historical sample trajectory data at the intersection according to the approach lanes to form a set A of longitude and latitude coordinates of vehicle trajectory points for each lane. l , l ∈ L, where L represents the set of intersection lanes. Use the Kmeans clustering method to classify the trajectory data of the lanes within the physical area of the intersection, and obtain K trajectory point clusters D(l [1-K] ) and K historical vehicle clusters V(l [1-K] ), where l [1-K] represents the 1 to K different turns of lane l;

[0010] Step S-2: Identification of trajectory conflicts between lanes, including the following steps:

[0011] Step S-2-1: Identification of trajectory conflict points between different lanes:

[0012] Based on the historical flow trajectory data of different lanes, judge whether there is a conflict in the trajectory by determining whether the distance between the nearest point pairs between the trajectory clusters is less than the conflict distance threshold cofThr, where: respectively represent the kth trajectory of lane l1 and the kth trajectory of lane l2;

[0013] Step S-2-2: Calculation of the trajectory conflict index between different lanes, including the following steps:

[0014] Step S-2-2-1: Discrimination of conflict types between different flows of a lane:

[0015] The angle enA(l k ) of the approach of lane l to the intersection physical area and the exit angle exA(l k ) of the kth trajectory of lane l after leaving the physical area can be calculated based on the heading angles of all trajectory points before entering the intersection physical area, and then judge the conflict type between different trajectories of the lane, as shown in the following formula:

[0016]

[0017] In the formula: represents the conflict type between the kth trajectory of lane l1 and the kth trajectory of lane l2; 1 represents a diverging conflict, 2 represents a converging conflict, and 3 represents an intersecting conflict;

[0018] Step S-2-2-2: Calculation of the conflict index between different flows of a lane:

[0019] Calculate the conflict degree of two tracks based on the number of conflict points, conflict point types, conflict crossing angles, conflict speeds, large vehicle ratios in conflicts, and conflict perspectives, and define the calculated conflict degree as the conflict index: the larger the conflict index, the higher the conflict degree between the two tracks, and the smaller the conflict index, the more suitable the two are for simultaneous release in one phase, where:

[0020] The number of conflict points represents the number of point pairs with a distance between coordinates less than the conflict distance threshold cofThr in the two flow trajectories;

[0021] The conflict crossing angle represents the difference in the heading angles of the conflict points of the two flow trajectories. Among them, the impact of the crossing angle on safety varies for different conflict types: in the case of diverging conflicts, the smaller the conflict crossing angle, the easier it is for vehicle rear-end collisions to occur, and the lower the conflict safety; in the case of crossing and merging conflicts, the larger the conflict crossing angle, the higher the severity of the conflict;

[0022] The conflict speed represents the relative speed situation of the two flows when a conflict is formed. Among them, the impact of the vehicle speeds of the two vehicles on safety varies for different conflict types: in the case of diverging and merging conflicts, the greater the speed difference between the two conflict points of the trajectories, the higher the risk; in the case of crossing conflicts, the greater the combined value of the conflict speeds of the two trajectories, the higher the severity of the conflict;

[0023] The large vehicle ratio in conflict represents the large vehicle composition of the two conflicting traffic flows;

[0024] The conflict perspective coefficient represents the degree to which each of the two vehicles can perceive the other traffic flow before entering the intersection. The larger the conflict perspective coefficient, the greater the potential safety hazard;

[0025] Step S-2-2-3: Calculation of the conflict index between lanes:

[0026] The conflict index between lanes represents the sum of the conflict degrees between all flows between two lanes, which is composed of the conflict coefficients between the corresponding flows of the lanes, as shown in the following formula:

[0027]

[0028] In the formula: cofF(l1, l2) represents the conflict index between lane l1 and lane l2, is the conflict coefficient between the k flow of lane l1 and the k flow of lane l2 obtained in step S-2-2-2;

[0029] Step S-2-3: Calculation of the green interval time of the lane, as shown in the following formula:

[0030]

[0031] In the formula: represents the number of conflict points between the k flow of lane l1 and the k flow of lane l2; Indicates the green interval time between the flow direction k of lane l1 and the flow direction k of lane l2; Indicates the distance between the stop line of lane l1 and the conflict point between the flow direction k of lane l1 and the flow direction k of lane l2; lInt(l1, l2) indicates the green interval time between lane l1 and lane l2;

[0032] Step S-3: Lane merging basic flow directions, including the following steps:

[0033] Step S-3-1: Construct basic flow directions:

[0034] For each approach lane function at the intersection, construct a set F of basic flow directions, and the flow direction f m The corresponding lane set is FL fm ;

[0035] Step S-3-2: Calculate the flow rate ratio of basic flow directions:

[0036] Calculate the flow rate ratio of the key lane of the flow direction according to the flow rate ratios of all lanes corresponding to the basic flow direction, as shown in the following formula:

[0037] fR(f) = max(lR(l)) l ∈ FL f

[0038] In the formula: fR represents the set of flow rate ratios of the key lanes of the flow direction;

[0039] Step S-3-3: Construct a conflict matrix of basic flow directions:

[0040] For the basic flow directions obtained in Step S-3-1, calculate the conflict coefficients between all basic flow directions, and construct a flow direction conflict matrix CF, as shown in the following formula:

[0041] CF = [cf(f m , f m )] M×M

[0042]

[0043] In the formula: M represents the number of basic flow directions; cf(f1, f2) represents the conflict coefficient between flow direction f1 and flow direction f2;

[0044] Step S-3-4: Construct a compatibility coefficient matrix of basic flow directions:

[0045] Quantitatively analyze the compatibility between all basic flow directions to determine which flow directions are suitable for simultaneous release, as shown in the following formula:

[0046] CP = [cp(f m , f m )] M×M

[0047]

[0048] In the formula: CP represents the compatibility matrix between all basic flow directions at the intersection; cp(f1, f2) represents the compatibility coefficient between flow direction f1 and flow direction f2;

[0049] Step S-3-5: Calculation of the green interval time for flow directions:

[0050] Calculate the green interval time between adjacent flow directions according to the green interval time of the lanes corresponding to the flow directions, as shown in the following formula:

[0051]

[0052] In the formula: fInt(f1, f2) represents the green interval time between flow direction f1 and flow direction f2 when they are adjacent front and back flow directions respectively;

[0053] Step S-4: Optimization of the basic phases, including the following steps:

[0054] Step S-4-1: Merging the basic phases of flow directions:

[0055] Based on the compatibility coefficients between all basic flow directions obtained in step S-3-4, calculate the modularity of the phase after merging the flow directions, and output the flow direction combination with the highest modularity as the basic phase set BP, and the basic flow directions corresponding to the phase bp q The corresponding set of basic flow directions is

[0056] Step S-4-2: Calculation of the critical flow direction and critical flow rate ratio of the basic phase:

[0057] Obtain the flow direction f with the largest critical flow rate ratio * As the critical flow direction of the phase, the corresponding critical flow rate ratio is used as the critical flow rate ratio of the phase, as shown in the following formula:

[0058] pR(bp) = max(fR(f)) f ∈ PF bp

[0059] pKF(bp) = f *

[0060] In the formula: pR represents the set of critical flow rate ratios of the basic phase; pKF represents the set of critical flow directions of the basic phase;

[0061] Remove the critical flow direction f from the set of flow directions of the basic phase * Establish the set of non-critical flow directions PSF of the phase bp And calculate the flow rate ratio of the non-critical flow directions of the phase, as shown in the following formula:

[0062] pSR(bp) = max(fR(f)) f ∈ PSFbp

[0063] In the formula, pSR represents the set of base phase non-critical flow rate ratios;

[0064] Step S-4-3: Calculation of base phase critical flow direction compatibility:

[0065] Perform secondary clustering on the critical flow directions of each base phase to form new phases, which specifically includes the following steps:

[0066] Step S-4-3-1: Calculate the critical flow direction compatibility between base phases:

[0067] Establish a calculation method for the degree of overlap between two phases through the difference in the flow rate ratios of the primary and secondary flow directions of the phases, and use this as the compatibility of the critical flow directions of the two phases. Otherwise, set the compatibility of the critical flow directions of the two phases to 0, as shown in the following formula:

[0068]

[0069] In the formula: kcf(bp1,bp2) represents the critical flow direction compatibility between base phase bp1 and base phase bp2;

[0070] Step S-4-3-2: Generate basic phases by overlapping base phases:

[0071] Based on the compatibility coefficients between the critical flow directions of all phases, further use the Newman algorithm to iteratively calculate the phase modularity under different phase overlap schemes, select the overlap scheme with the highest modularity, output the result as the overlap phase set LP, and merge the overlap phase set LP with the base phase set to form the basic phase set P. The number of basic phases is O;

[0072] Step S-5: Phase sequence optimization, including the following steps:

[0073] Step S-5-1: Calculation of the green light interval time matrix for phases:

[0074] Calculate the green light interval time between adjacent phases according to the green light interval time between the base flow directions that make up the basic phases, as shown in the following formula:

[0075]

[0076] In the formula: pInt(p1,p2) represents the green light interval time between the flow directions p1 and p2 as the front and rear adjacent phases respectively;

[0077] Step S-5-2: Generation of the phase sequence scheme:

[0078] Taking the minimization of the sum of the green intervals between all phases as the goal, considering the phase sequence constraints between the overlapping phases and the basic phases described in step S-4-3-2, the optimal phase sequence plan sP is optimized by the exhaustive method. * , as shown in the following formula:

[0079] sP = [sp o O = {sp1, sp2, … sp O}

[0080]

[0081] In the formula: L represents the sum of the green intervals of all phases.

[0082] Preferably, the step S-1 includes the following steps:

[0083] Step S-1-1: Obtain the basic lane data, which includes the number of lanes N, the lane functions of all import lanes, and the cross-section coordinates of the import lanes:

[0084] L = [l n N = {l1, l2, … l N}

[0085] In the formula: L represents the set of intersection lanes, and l n represents the nth lane of the intersection;

[0086] Step S-1-3: Calculate the lane flow ratio:

[0087] The lane flow ratio is the ratio of the lane hourly flow Q(l) to the design saturation traffic volume S(l), and its calculation formula is as follows:

[0088]

[0089] In the formula: lR represents the set of lane flow ratios;

[0090] Step S-1-3: Integrate the lane trajectory data:

[0091] The lane trajectory data is the single-vehicle trajectory data after holographic fusion at the intersection. Classify a certain number of historical sample trajectory data at the intersection according to the import lanes to form the longitude and latitude coordinate set A of the vehicle trajectory points divided by lanes l , l ∈ L; Step S-1-4: Lane coordinate transformation:

[0092] Based on the Gaussian projection, use the 3° zone method to transform the longitude and latitude coordinates of the trajectory data into Cartesian coordinates, and further move the calculation interval to the vicinity of the origin through coordinate translation to facilitate subsequent calculations, thereby forming a new coordinate set D l ​​, l ∈ L;

[0093] Step S-1-5: Lane trajectory data cleaning and classification:

[0094] Use the Kmeans clustering method to classify the trajectory data of the lane in the physical area of the intersection. The number of turns corresponding to the lane is used as the K value for clustering. After removing abnormal trajectory data, K trajectory clusters D(l [1-K] ) and K historical vehicle clusters V(l [1-K] ) can be obtained.

[0095] Preferably, in the step S-2-1, the closest pair of points is the coordinates of the two closest points in the two trajectory clusters of lane l1 and lane l2, and is solved by traversing the coordinates of the trajectory points of the two lanes and calculating the distance between them.

[0096] Preferably, in the step S-2-1, the number of conflict points W is related to the number of flow directions included in the two lanes:

[0097]

[0098] In the formula, are the number of flow directions of lane l1 and lane l2 respectively.

[0099] Preferably, in step S-2-2-2:

[0100] The number of conflict points between the flow direction k of lane l1 and the flow direction k of lane l2 is represented by . The more conflict points, the greater the conflict risk, that is, the greater the conflict coefficient. For unified calculation, it is standardized, so there is:

[0101]

[0102] In the formula represents the conflict number coefficient between the flow direction k of lane l1 and the flow direction k of lane l2;

[0103] The conflict angle coefficient represents the safety coefficient corresponding to the conflict angle between the two flow directions. The greater the conflict risk, the higher the angle coefficient. The standardized calculation formula is as follows:

[0104]

[0105] In the formula: represents the conflict angle coefficient between the flow direction k of lane l1 and the k flow direction of lane l2; coF represents the conflict coefficient, and the conflict coefficient takes different values under different conflict types due to the difference in danger level;

[0106] It represents the conflict speed coefficient of the flow direction k of lane l1 and the k flow direction of lane l2. The higher the conflict speed risk, the higher the conflict speed coefficient. Then, we have:

[0107]

[0108] In the formula:

[0109]

[0110]

[0111] speed(p) represents the point speed of the trajectory point p; maxSpeed represents the maximum value of the speeds of all trajectory points at the intersection; It represents the speed difference between the flow direction k of lane l1 and the flow direction k of lane l2; It represents the sum of the speeds of the flow direction k of lane l1 and the k flow direction of lane l2;

[0112] It represents the conflict large vehicle ratio coefficient between the flow direction k of lane l1 and the flow direction k of lane l2. Then, we have:

[0113]

[0114] In the formula: truckRatio(l k ) represents the large vehicle ratio of the flow direction k of lane l; num(V(l k )) represents the total number of vehicles in the flow direction k of lane l; truckNum(V(l k ) represents the total number of large vehicles in the flow direction k of lane l;

[0115] It represents the conflict perspective coefficient between the flow direction k of lane l1 and the k flow direction of lane l2. Then, we have:

[0116]

[0117] It represents the conflict coefficient between the flow direction k of lane l1 and the k flow direction of lane l2. Then, we have:

[0118]

[0119] In the formula: cofNumF, angleF, speedF, truckF, sightF, cofN are respectively the simplified representations; NThr represents the conflict point quantity threshold.

[0120] The present invention quantitatively determines the conflict degree of each flow direction from multiple aspects such as the type, quantity, angle, speed, vehicle type composition, and line of sight angle of the conflict points between flow directions through the high-precision trajectory data of historical vehicles at intersections. On this basis, the key flow rate ratios of each flow direction are combined to flexibly combine each flow direction, and the basic control phase unit of the intersection is determined. Finally, the present invention calculates the phase interval time considering the conflict of the trailing vehicle between the front and rear phases, and determines the release order of the phase units with the goal of minimizing the sum of the required clearance times between all adjacent phases. In this way, the subjectivity of manual intervention during the phase sequence setting can be avoided, the labor cost can be reduced, the rationality of the signal plan setting can be improved, and it is convenient for practical promotion.

[0121] The present invention can optimize the control of the signal phase and sequence at intersections based on intersection trajectory data. For the complex signal intersection phase sequence design problem, the conflict degree of each flow direction is quantitatively determined through high-precision trajectory data, and the composition of the phase unit and its release phase sequence are optimized by combining the key flow rate ratio of the flow direction and the green interval time data, effectively reducing the subjectivity of the phase sequence design. The design method that fully considers safety and efficiency can make full use of the intersection space-time resources, improve the vehicle operation efficiency while avoiding key conflicts, and make the urban signal control more scientific, reasonable, and flexible. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] Figure 1 is a schematic flow chart of the method for designing the signal phase and sequence at intersections according to the present invention;

[0123] Figure 2 is a schematic diagram for identifying the conflict points of each flow direction of two lanes according to the present invention;

[0124] Figure 3 is a schematic diagram of various conflict types at intersections according to the present invention;

[0125] Figure 4 is a schematic diagram of the conflict angle and the conflict driving-in perspective for each flow direction according to the present invention, which is used for quantitative analysis of the conflict degree;

[0126] Figure 5 is a schematic diagram of the distance between the starting point of each flow direction and the conflict point according to the present invention, which is used for the calculation of the green interval time

[0127] Figure 6 is a schematic flow chart of the process of merging basic flow directions into phases by the community clustering method according to the present invention;

[0128] Figure 7 is a schematic diagram of overlapping the key flow directions of the basic phases to form a new phase according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0129] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0130] This embodiment discloses a signal intersection phase and sequence optimization method based on a trajectory conflict coefficient, including the following steps:

[0131] Step S-1: Trajectory data preprocessing, including the following steps:

[0132] Step S-1-1: Obtain lane basic data, where the lane basic data includes the number of lanes N, the lane functions of all import lanes, and the cross-section coordinates of the import lane:

[0133] L = [l n N = {l1, l2,... l N}

[0134] In the formula: L represents the intersection lane set, and l n represents the nth lane at the intersection.

[0135] Step S-1-3: Calculate the lane flow rate ratio:

[0136] The lane flow rate ratio is the lane saturation flow rate, which refers to the ratio of the lane hourly flow Q(l) to the design saturation traffic volume S(l). The calculation formula is as follows:

[0137]

[0138] In the formula: lR represents the lane flow rate ratio set.

[0139] Step S-1-3: Integrate lane trajectory data:

[0140] The lane trajectory data is the single-vehicle trajectory data after holographic fusion at the intersection. A certain number of historical sample trajectory data at the intersection are classified according to the import lanes to form a set A of longitude and latitude coordinates of vehicle trajectory points for each lane l , l ∈ L.

[0141] Step S-1-4: Lane coordinate transformation:

[0142] Based on Gauss projection, the present invention uses the 3° zone method to convert the longitude and latitude coordinates of the trajectory data into Cartesian coordinates, and further moves the calculation interval to the vicinity of the origin through coordinate translation for convenient subsequent calculation, thereby forming a new coordinate set D l , l ∈ L.

[0143] Step S-1-5: Lane trajectory data cleaning and classification:

[0144] Considering that there are multiple turns in an actual single lane, the present invention uses the Kmeans clustering method to classify the trajectory data of the lane in the physical area of the intersection, where the number of turns corresponding to the lane is used as the K value for clustering. After removing abnormal trajectory data, K trajectory clusters D(l [1-K] ) and K historical vehicle clusters V(l [1-K] ) included in the lane can be obtained.

[0145] Step S-2: Identification of trajectory conflicts between lanes, including the following steps:

[0146] Step S-2-1: Identification of trajectory conflict points between different lanes:

[0147] Based on the historical flow trajectory data of different lanes, the present invention determines whether there is a conflict in the trajectory by judging whether the distance between the nearest point pairs of the trajectory clusters is less than the conflict distance threshold cofThr, where: respectively represent the k-th trajectory of lane l1 and the k-th trajectory of lane l2. The nearest point pair is the coordinates of the two points with the closest distance in the two trajectory clusters of lane l1 and lane l2 (i.e., in the two coordinate sets), and is solved by traversing the coordinates of the trajectory points of the two lanes and calculating the distance between them.

[0148] The number of conflict points W is related to the number of flow directions included in the two lanes:

[0149]

[0150] In the formula, are respectively the number of flow directions of lane l1 and lane l2.

[0151] Step S-2-2: Calculation of the trajectory conflict index between different lanes, including the following steps:

[0152] Step S-2-2-1: Discrimination of conflict types between each flow direction of the lane:

[0153] For lane l, the angle enA(l k ) of the inlet where the lane is located can be calculated according to the heading angles of all trajectory points before entering the physical area of the intersection, and the exit angle exA(l k ) of the k-th trajectory of lane l after leaving the physical area.

[0154] The conflict type between different trajectories of the lane can be determined according to the inlet and outlet angles of the trajectory.

[0155]

[0156] In the formula: Indicates the conflict type between the k-th trajectory of lane l1 and the k-th trajectory of lane l2; 1 indicates a diverging conflict, 2 indicates a merging conflict, and 3 indicates an intersection conflict; Aabs() represents the absolute deviation of two direction angles.

[0157] Step S-2-2-2: Calculation of the conflict index between the flows of each lane:

[0158] It is characterized in that the conflict degree between two trajectories is calculated based on the number of conflict points, conflict point type, conflict intersection angle, conflict speed, conflict large vehicle ratio, and conflict perspective, and the calculated conflict degree is defined as the conflict index. The larger the conflict index, the higher the conflict degree between the two trajectories, and the smaller the conflict index, the more suitable the two are for being released simultaneously in one phase.

[0159] The number of conflict points represents the number of point pairs with a distance between coordinates less than the conflict distance threshold cofThr in the trajectories of two flows, and is represented by The more the number of conflict points, the greater the conflict risk, that is, the greater the conflict coefficient. For unified calculation and standardization, there is:

[0160]

[0161] In the formula: Represents the conflict number coefficient between the k-th flow of lane l1 and the k-th flow of lane l2.

[0162] The conflict intersection angle mentioned above represents the difference in the course angles of the conflict points of the trajectories of two flows. Among them, the influence of the intersection angle on safety is different for different conflict types. In the case of a diverging conflict, the smaller the conflict intersection angle, the easier it is for vehicle rear-end collisions to occur, and the lower the conflict safety. In the case of an intersection conflict and a merging conflict, the larger the conflict intersection angle, the higher the severity of the conflict. According to relevant research results, the conflict risk levels of different conflict types are different. The intersection conflict has the highest risk level, the merging conflict has the second highest, and the diverging conflict has the lowest. The conflict angle coefficient represents the safety coefficient corresponding to the conflict angle of two flows. The greater the conflict risk, the higher the angle coefficient. The standardized calculation formula is as follows:

[0163]

[0164] In the formula: Represents the conflict angle coefficient between the k-th flow of lane l1 and the k-th flow of lane l2; coF represents the conflict coefficient. Due to the difference in risk levels, the conflict coefficient values are different for different conflict types. Under the diverging conflict, coF = 0.3, under the merging conflict, coF = 0.6, and under the intersection conflict, coF = 1.0.

[0165] Conflict speed represents the relative speed of two flowing directions when a conflict occurs. Under different conflict types, the impact of the speeds of two vehicles on safety varies. During diverging and merging conflicts, the greater the speed difference at the two trajectory conflict points, the higher the risk level. During crossing conflicts, the greater the combined value of the two trajectory conflict speeds, the higher the severity of the conflict.

[0166]

[0167] Where: speed(p) represents the point speed of trajectory point p; maxSpeed represents the maximum speed of all trajectory points at the intersection, with the unit of m / s; represents the speed difference between the flowing direction k of lane l1 and the flowing direction k of lane l2, with the unit of m / s; represents the sum of the speeds of the flowing direction k of lane l1 and the k flowing direction of lane l2, in m / s; represents the conflict speed coefficient between the flowing direction k of lane l1 and the k flowing direction of lane l2. The higher the risk level of the conflict speed, the higher the conflict speed coefficient.

[0168] The conflict large vehicle ratio represents the large vehicle composition of two conflicting traffic flows. Since large vehicles have greater inertia and larger blind spots when turning, their safety is relatively low. Therefore, when considering the conflict of two vehicle flows, the vehicle composition of the vehicle flows needs to be considered. The larger the proportion of large vehicles in the two trajectories, the larger the conflict large vehicle ratio coefficient.

[0169]

[0170] Where: represents the conflict large vehicle ratio coefficient between the flowing direction k of lane l1 and the flowing direction k of lane l2; truckRatio(l k ) represents the large vehicle ratio of the flowing direction k of lane l; num(V(l k )) represents the total number of vehicles in the flowing direction k of lane l; truckNum(V(l k ) represents the total number of large vehicles in the flowing direction k of lane l.

[0171] The conflict perspective coefficient represents the degree to which two vehicles can respectively perceive the other vehicle flow before entering the intersection. Before driving into the intersection, compared with the vehicles at the cross-entrance, the driver is more likely to find the target directly in front. Therefore, the closer the course angle difference between the two flowing directions before entering the intersection is to 0° or 180°, the higher the safety. The closer the angle difference is to 90°, the larger the blind spot and the lower the safety. The larger the conflict perspective coefficient, the greater the potential safety hazard.

[0172]

[0173] Where: represents the conflict perspective coefficient between the flowing direction k of lane l1 and the k flowing direction of lane l2.

[0174] As described above, the degree of trajectory conflict is affected by the number of conflict points, the type of conflict points, the conflict intersection angle, the conflict speed, the conflict large vehicle ratio, and the conflict perspective. Therefore, the conflict coefficient is composed of the number of conflict points, the conflict angle coefficient, the conflict speed coefficient, the conflict large vehicle ratio coefficient, and the conflict perspective coefficient.

[0175]

[0176] In the formula: represents the conflict coefficient between the flow direction k of lane l1 and the k flow direction of lane l2; cofNumF, angleF, speedF, truckF, sightF, cofN are respectively The simplified representation of; NThr represents the conflict point quantity threshold.

[0177] Step S-2-2-3: Calculation of the conflict index between lanes:

[0178] The conflict index between lanes represents the sum of the conflict degrees between all flow directions between two lanes, and is composed of the conflict coefficients between the corresponding flow directions of the lanes, as shown in the following formula:

[0179]

[0180] In the formula: cofF(l1, l2) represents the conflict index between lane l1 and lane l2.

[0181] Step S-2-3: Calculation of the green light interval time of the lane

[0182] The green light interval time represents the time interval between the end time of the green light of one of the two lanes and the start time of the green light of the next lane. This parameter is an important parameter to ensure the safe clearance of vehicles on the previous lane. Using the trajectory data, the distance d between the stop line and the conflict point and the average speed when the vehicle passes can be obtained, and then the green light interval time between each lane can be calculated, as shown in the following formula:

[0183]

[0184] In the formula: represents the green light interval time between the flow direction k of lane l1 and the flow direction k of lane l2; represents the distance between the stop line of lane l1 and the conflict point between the flow direction k of lane l1 and the flow direction k of lane l2; lInt(l1, l2) represents the green light interval time between lane l1 and lane l2.

[0185] Step S-3: Lane merging basic flow directions, including the following steps:

[0186] Step S-3-1: Construct the basic flow directions:

[0187] The basic flow refers to the lane groups with the same characteristics flowing out of the same approach lane. A basic flow is the basic unit of signal control. For non-exclusive lanes, they are uniformly regarded as a whole basic flow. According to the lane functions of each approach lane described in step S-1-1 for the intersection, a basic flow set F and a flow f are constructed. m The corresponding lane set is FL. fm , then there is:

[0188] F = [f m M = {f1, f2,... f M}

[0189] In the formula: M represents the number of basic flows.

[0190] Step S-3-2: Calculate the basic flow rate ratio:

[0191] Calculate the critical lane flow rate ratio of the flow according to all the lane flow rate ratios corresponding to the basic flow, as shown in the following formula:

[0192] fR(f) = max(lR(l)) l ∈ FL f

[0193] In the formula: fR represents the set of critical lane flow rate ratios of the flow.

[0194] Step S-3-3: Construct the basic flow conflict matrix:

[0195] For the basic flows obtained in step S-3-1, calculate the conflict coefficients between all basic flows and construct a flow conflict matrix CF. The conflict coefficient between two basic flows can be obtained by adding the conflict coefficients between all lanes corresponding to the two flows, as shown in the following formula:

[0196] CF = [cf(f m , f m )] M×M

[0197]

[0198] In the formula: cf(f1, f2) represents the conflict coefficient between flow f1 and flow f2.

[0199] Step S-3-4: Construct the basic flow compatibility coefficient matrix:

[0200] To obtain the most reasonable basic phase, the present invention conducts a quantitative analysis on the compatibility between all basic flows to determine which flows are suitable for simultaneous release, as shown in the following formula:

[0201] CP = [cp(f m , fm )] M×M

[0202]

[0203] Where: CP represents the compatibility matrix between all basic flow directions at the intersection; cp(f1, f2) represents the compatibility coefficient between flow direction f1 and flow direction f2.

[0204] Step S-3-5: Calculation of the green interval time for flow directions:

[0205] Calculate the green interval time between adjacent flow directions based on the green interval time of the lanes corresponding to the flow directions, as shown in the following formula:

[0206]

[0207] Where: fInt(f1, f2) represents the green interval time between flow direction f1 and flow direction f2 when they are adjacent front and back flow directions respectively.

[0208] Step S-4: Optimization of the basic phases, including the following steps:

[0209] Step S-4-1: Merging of flow directions for the basic phases:

[0210] The merging of basic flow directions refers to a set of flow directions formed by combining a group of flow directions with less conflict from the perspective of flow direction conflict relationships. To obtain a more reasonable basic phase, the present invention refers to the idea of clustering analysis of the traditional Newman algorithm. Based on the compatibility coefficients between all basic flow directions obtained in step S-3-4, calculate the modularity of the phase after merging the flow directions, and output the flow direction combination with the highest modularity as the basic phase set BP, and the phase bp q The corresponding set of basic flow directions is Then there is:

[0211] BP = [bp q Q = {bp1, p2,... bp Q}

[0212] Step S-4-2: Calculation of the key flow direction and the key flow rate ratio for the basic phases:

[0213] The key flow rate ratio of a phase refers to the maximum flow rate ratio of all flow directions corresponding to the phase. According to the key flow rate ratios of all flow directions corresponding to the basic phase, find the flow direction f * as the key flow direction of the phase, and its corresponding key flow rate ratio as the key flow rate ratio of the phase, as shown in the following formula:

[0214] pR(bp) = max(fR(f)) f ∈ PF bp ​

[0215] pKF(bp) = f *

[0216] Where: pR represents the set of base phase critical flow rate ratios; pKF represents the set of base phase critical flow directions.

[0217] To further study the non-critical flow directions of the base phase, the critical flow direction f is removed from the set of base phase flow directions * Establish a set of non-critical flow directions of the phase PSF bp And calculate the flow rate ratio of the non-critical flow directions of the phase, as shown in the following formula:

[0218] pSR(bp) = max(fR(f)) f ∈ PSF bp

[0219] Wherein, pSR represents the set of base phase non-critical flow rate ratios.

[0220] Step S-4-3: Calculation of the compatibility of the critical flow directions of the base phase:

[0221] For the base phase obtained from the flow direction combination, a base flow direction can only operate alone in one base phase. However, due to the difference in the flow rate ratios of each flow direction in the same phase, it is easy to cause waste of time and space resources. The present invention performs secondary clustering on the critical flow directions of each base phase, thereby overlapping new phases, which specifically includes the following steps:

[0222] Step S-4-3-1: Calculate the compatibility of the critical flow directions between the base phases:

[0223] For the critical flow directions between each base phase, if the flow directions do not conflict, the present invention establishes a calculation method for the degree of overlap between two phases by the difference in the flow rate ratios of the primary and secondary flow directions of the phase, and uses this as the compatibility of the critical flow directions of the two phases. Otherwise, the compatibility of the critical flow directions of the two phases is set to 0, as shown in the following formula:

[0224]

[0225] Where: kcf(bp1, bp2) represents the compatibility of the critical flow directions of the base phase bp1 and the base phase bp2.

[0226] Step S-4-3-2: Generate basic phases by overlapping the base phases:

[0227] Based on the compatibility coefficients between all phase critical flow directions, further use the Newman algorithm to iteratively calculate the phase modularity under different phase overlapping schemes, and select the overlapping scheme with the highest modularity and output the result as the overlapping phase set LP.

[0228] Further merge the overlapping phase set and the basic phase set into the basic phase set P, and the number of basic phases is O. Among them, in order to ensure the operation continuity of the basic flow, the phase sequence of the overlapping phase and its basic phase is fixed.

[0229] Step S-5: Phase sequence optimization, including the following steps:

[0230] Step S-5-1: Calculation of the green interval time matrix for phases:

[0231] Calculate the green interval time between adjacent phases according to the green interval time between the basic flows that make up the basic phase, as shown in the following formula:

[0232]

[0233] In the formula: pInt(p1, p2) represents the green interval time between the flow directions p1 and p2 as the front and rear adjacent phases respectively.

[0234] Step S-5-2: Generation of the phase sequence scheme:

[0235] Due to the geometric characteristics of the intersection, the required green interval times between the basic flow directions are different. In order to maximize the traffic capacity of the intersection, the phase loss time should be minimized as much as possible. Therefore, the present invention aims to minimize the sum of the green interval times between all phases, and considers the phase sequence constraints of the overlapping phase and the basic phase described in step S-4-3-2, and uses the exhaustive method to optimize and obtain the optimal phase sequence scheme sP * , as shown in the following formula:

[0236] sP = [sp o O = {sp1, sp2,... sp O}

[0237]

[0238] In the formula: L represents the sum of the green interval times of all phases.​

Claims

1. An automatic optimization method for signal phase and sequence based on trajectory conflict index, characterized in that It includes the following steps: Step S-1: Preprocessing of trajectory data: The lane trajectory data is the single-vehicle trajectory data after intersection holographic fusion. A certain number of historical sample trajectory data at the intersection are classified according to the import lanes to form a set A of longitude and latitude coordinates of vehicle trajectory points for each lane. l , l ∈ L, where L represents the set of intersection lanes. The Kmeans clustering method is used to classify the trajectory data of the lanes within the physical area of the intersection, and K trajectory point clusters D(l [1-K] ) and K historical vehicle clusters V(l [1-K] ) are obtained. Among them, l [1-K] represents the 1 to K different turns of lane l. Step S-2: Identification of trajectory conflicts between lanes, including the following steps: Step S-2-1: Identification of conflict points of trajectories in different lanes: Based on the historical flow trajectory data of different lanes, by judging the distance between the nearest point pairs of trajectory clusters to determine whether there is a conflict in the trajectory by judging whether the distance is less than the conflict distance threshold cofThr, where: respectively represent the k-th trajectory of lane l1 and the k-th trajectory of lane l2; Step S-2-2: Calculation of the trajectory conflict index between different lanes, including the following steps: Step S-2-2-1: Discrimination of conflict types between different flow directions of a lane: Lane l can calculate the angle enA(l of the entrance of the lane l according to the heading angles of all trajectory points before entering the physical area of the intersection k ), and the exit angle exA(l of the trajectory k of lane l after leaving the physical area k ), and then judge the conflict type between different trajectories of the lane, as shown in the following formula: In the formula: represents the conflict type between the k-th trajectory of lane l1 and the k-th trajectory of lane l2; 1 represents a diverging conflict, 2 represents a merging conflict, and 3 represents an intersection conflict; Step S-2-2-2: Calculation of the conflict index between different flow directions of a lane: Based on the number of conflict points, conflict point types, conflict intersection angles, conflict speeds, conflict large vehicle ratios, and conflict perspectives of two trajectories, calculate the conflict degree between the two trajectories, and define the calculated conflict degree as the conflict index: The larger the conflict index, the higher the conflict degree between the two trajectories, and the smaller the conflict index, the more suitable the two are for being released simultaneously in one phase. Among them: The number of conflict points represents the number of point pairs with a distance between coordinates in the trajectories of two flow directions less than the conflict distance threshold cofThr; The conflict intersection angle represents the difference in the heading angles of the conflict points of the trajectories of two flow directions. Among them, the influence of the intersection angle on safety varies for different conflict types: In the case of diverging conflicts, the smaller the conflict intersection angle, the easier it is for vehicle rear-end collisions to occur, and the lower the conflict safety; In the case of crossing conflicts and merging conflicts, the larger the conflict intersection angle, the higher the severity of the conflict; The conflict speed represents the relative speed situation of the two flow directions when a conflict occurs. Among them, the influence of the speeds of the two vehicles on safety varies for different conflict types: In the case of diverging and merging conflicts, the greater the speed difference between the two conflict points of the trajectories, the higher the risk; In the case of crossing conflicts, the greater the combined value of the conflict speeds of the two trajectories, the higher the severity of the conflict; The conflict large vehicle ratio represents the composition of large vehicles in the two conflicting traffic flows; The conflict perspective coefficient represents the degree to which two vehicles can perceive the other traffic flow before entering the intersection respectively. The larger the conflict perspective coefficient, the greater the potential safety hazard; Step S-2-2-3: Calculation of the conflict index between lanes: The conflict index between lanes represents the sum of the conflict degrees between all flow directions between two lanes, and is composed of the conflict coefficients between the corresponding flow directions of the lanes, as shown in the following formula: Where: cofF(l1, l2) represents the conflict index between lane l1 and lane l2, which is the conflict coefficient between the traffic flow direction k of lane l1 obtained in step S-2-2-2 and the traffic flow direction k of lane l2; Step S-2-3: Calculation of the green interval time of the lane, as shown in the following formula: In the formula: represents the number of conflict points between the traffic flow direction k of lane l1 and the traffic flow direction k of lane l2; represents the green light interval time between the traffic flow direction k of lane l1 and the traffic flow direction k of lane l2; represents the distance between the stop line of lane l1 and the conflict point between the traffic flow direction k of lane l1 and the traffic flow direction k of lane l2; lInt(l1, l2) represents the green light interval time between lane l1 and lane l2; Step S-3: Merging the basic flow directions of the lanes, including the following steps: Step S-3-1: Constructing the basic flow directions: Construct a basic flow direction set F and a flow direction f according to the lane functions of each approach at the intersection m The corresponding lane set is Step S-3-2: Calculating the flow rate ratio of the basic flow directions: Calculate the flow rate ratio of the key lanes of the flow direction according to the flow rate ratios of all lanes corresponding to the basic flow direction, as shown in the following formula: fR(f) = max(lR(l)) where l ∈ FL f In the formula: fR represents the set of flow rate ratios of the key lanes of the flow direction; Step S-3-3: Constructing the conflict matrix of the basic flow directions: For the basic flow directions obtained in Step S-3-1, calculate the conflict coefficients between all basic flow directions, and construct the flow direction conflict matrix CF, as shown in the following formula: CF = [cf(f m , f m )] M×M In the formula: M represents the number of basic flow directions; cf(f1, f2) represents the conflict coefficient between flow direction f1 and flow direction f2; Step S-3-4: Constructing the compatibility coefficient matrix of the basic flow directions: Quantitatively analyze the compatibility between all basic flow directions to determine which flow directions are suitable for being released simultaneously, as shown in the following formula: CP = [cp(f m , f m )] M×M In the formula: CP represents the compatibility matrix between all basic flow directions at the intersection; cp(f1, f2) represents the compatibility coefficient between flow direction f1 and flow direction f2; Step S-3-5: Calculation of the green interval time for each traffic flow direction: Calculate the green interval time between adjacent traffic flow directions based on the green interval time for each lane corresponding to the traffic flow direction, as shown in the following formula: In the formula: fInt(f1, f2) represents the green interval time between the traffic flow directions f1 and f2 when they are adjacent to each other as the front and rear traffic flow directions respectively; Step S-4: Optimization of the basic phases, including the following steps: Step S-4-1: Combining traffic flow directions to form the basic phases: Based on the compatibility coefficients among all the basic flow directions obtained in step S-3-4, calculate the modularity of the phase after merging the flow directions, and output the flow direction combination with the highest modularity as the basic phase set BP and the phase bp q The corresponding basic flow direction set is Step S-4-2: Calculation of the critical traffic flow directions and the critical traffic flow rate ratios for the basic phases: Obtain the flow direction f with the maximum critical flow rate ratio * As the critical flow direction of the phase, the corresponding critical flow rate ratio is used as the critical flow rate ratio of the phase, as shown in the following formula: pR(bp) = max(fR(f)) for f ∈ PF bp pKF(bp) = f * In the formula: pR represents the set of critical traffic flow rate ratios for the basic phases; pKF represents the set of critical traffic flow directions for the basic phases; Eliminate the key flow f from the set of basic phase flow directions * Establish the set PSF of non-critical phase flow directions bp And calculate the flow rate ratio of non-critical phase flow directions as shown in the following formula: pSR(bp) = max(fR(f)) for f ∈ PSF bp In the formula, pSR represents the set of non-critical traffic flow rate ratios for the basic phases; Step S-4-3: Calculation of the compatibility of the critical traffic flow directions for the basic phases: Perform secondary clustering on the critical traffic flow directions of each basic phase to form new phases, specifically including the following steps: Step S-4-3-1: Calculation of the compatibility of the critical traffic flow directions between the basic phases: Establish a calculation method for the degree of overlap between two phases by using the difference in the traffic flow rate ratios of the primary and secondary traffic flow directions of the phases, and use this as the compatibility of the critical traffic flow directions between the two phases. Otherwise, set the compatibility of the critical traffic flow directions between the two phases to 0, as shown in the following formula: In the formula: kcf(bp1, bp2) represents the compatibility of the critical traffic flow directions between the basic phase bp1 and the basic phase bp2; Step S-4-3-2: Generating the basic phases by overlapping the basic phases: Based on the compatibility coefficients between the critical traffic flow directions of all phases, further use the Newman algorithm to iteratively calculate the modularity of the phase under different phase overlapping schemes, select the overlapping scheme with the highest modularity, output the result as the overlapping phase set LP, and merge the overlapping phase set LP with the basic phase set to form the basic phase set P, and the number of basic phases is O; Step S-5: Optimization of the phase sequence, including the following steps: Step S-5-1: Calculation of the matrix of the green interval time for the phases: Calculate the green interval time between adjacent phases based on the green interval time between the basic traffic flow directions that make up the basic phases, as shown in the following formula: In the formula: pInt(p1, p2) represents the green interval time between the traffic flow directions p1 and p2 when they are adjacent to each other as the front and rear adjacent phases respectively; Step S-5-2: Generating the phase sequence scheme: Taking the minimization of the sum of the green intervals between all phases as the objective, considering the phase sequence constraints between the lapped phase and the basic phase described in step S-4-3-2, the optimal phase sequence plan sP is obtained by exhaustive optimization * , as shown in the following formula: sP = [sp o O = {sp1, sp2,... sp O}​ In the formula: L represents the sum of the green interval times of all phases.

2. The automatic optimization method for signal phase and sequence based on trajectory conflict index according to claim 1, characterized in that The said Step S-1 includes the following steps: Step S-1-1: Obtain the basic lane data, which includes the number of lanes N, the lane functions of all import lanes, and the cross-section coordinates of the import lanes: L＝[l n ] N ＝{l1，l2，...l N} where: L represents the set of intersection lanes, and l n represents the nth lane at the intersection; Step S-1-3: Calculation of the lane traffic flow rate ratio: The lane traffic flow rate ratio is the ratio of the lane hourly traffic volume Q(l) to the design saturation traffic volume S(l), and its calculation formula is as follows: In the formula: lR represents the set of lane traffic flow rate ratios; Step S-1-3: Integration of the lane trajectory data: The lane trajectory data is the single-vehicle trajectory data after intersection holographic fusion. A certain number of historical sample trajectory data at the intersection are classified according to the approach lanes to form a set A of longitude and latitude coordinates of vehicle trajectory points for each lane l , l ∈ L; Step S-1-4: Conversion of the lane coordinates: Based on the Gaussian projection, use the 3° zone method to convert the longitude and latitude coordinates of the trajectory data into Cartesian coordinates, and further move the calculation interval to near the origin by coordinate translation to facilitate subsequent calculations, thereby forming a new coordinate set Dl, l ∈ L; Step S-1-5: Cleaning and classification of the lane trajectory data: The trajectory data of the lane in the physical area of the intersection is classified by the Kmeans clustering method, where the number of turns corresponding to the lane is used as the K value of the clustering. After removing the abnormal trajectory data, K trajectory clusters D(l [1-K] ) and K historical vehicle clusters V(l [1-K] ) included in the lane can be obtained.

3. The automatic optimization method for signal phase and sequence based on trajectory conflict index according to claim 1, wherein In the step S-2-1, the closest pair of points are the coordinates of the two points with the closest distance among the two trajectory clusters of lane l1 and lane l2, and are obtained by traversing the coordinates of the trajectory points of the two lanes and calculating the distance therebetween.

4. The automatic optimization method for signal phase sequence based on trajectory conflict index according to claim 1, wherein, In the step S-2-1, the number W of conflict points is related to the number of flow directions included in the two lanes: In the formula, are respectively the flow quantities of lane l1 and lane l2.

5. The automatic optimization method for signal phase sequence based on trajectory conflict index according to claim 4, characterized in that, In step S-2-2-2: The number of conflict points between the flow direction k of lane l1 and the flow direction k of lane l2 is represented by The more conflict points there are, the greater the conflict risk, that is, the greater the conflict coefficient. For unified calculation and standardization, we have: In the formula: represents the conflict number coefficient of the flow direction k of lane l1 and the flow direction k of lane l2; The conflict angle coefficient represents the safety coefficient corresponding to the conflict angle between two flow directions. The greater the conflict risk, the higher the angle coefficient. The standardized calculation formula is as follows: In the formula: represents the conflict angle coefficient of the flow direction k of lane l1 and the k flow direction of lane l2; coF represents the conflict coefficient, and the value of the conflict coefficient is different for different conflict types due to the difference in risk levels; It represents the conflict speed coefficient of the flow direction k of lane l1 and the k flow direction of lane l2. The higher the conflict speed risk degree, the higher the conflict speed coefficient. Thus, we have: In the formula: speed(p) represents the point speed of the trajectory point p; maxSpeed represents the maximum value of the speeds of all trajectory points at the intersection; represents the speed difference between the flow direction k of lane l1 and the flow direction k of lane l2; represents the sum of the speeds of the flow direction k of lane l1 and the k flow direction of lane l2; Denote the conflict large vehicle ratio coefficient of the flow direction k of lane l1 and the flow direction k of lane l2, then we have: where: truckRatio(l k ) represents the proportion of large vehicles in lane l flowing in the direction of k; num(V(l k )) represents the total number of vehicles in lane l flowing in the direction of k; truckNum(V(l k ) represents the total number of large vehicles in lane l flowing in the direction of k; Denote the conflict perspective coefficient of the flow direction k of lane l1 and the k flow direction of lane l2, then there is: Denote the conflict coefficient of the flow direction k of lane l1 and the k flow direction of lane l2, then we have: where: cofNumF, angleF, speedF, truckF, sightF, and cofN are the simplified representations of ; NThr represents the conflict point quantity threshold.

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