Regional boundary intersection signal control optimization method considering bus priority

Abstract

The invention discloses a region boundary intersection signal control optimization method considering bus priority. The method comprises the following steps: constructing a three-dimensional macroscopic fundamental diagram 3D-MFD based on historical traffic detection data; based on the current traffic detection data, in combination with 3D-MFD, judging whether region boundary intersection signal optimization control needs to be carried out or not; obtaining the total traffic adjustment amount of each intersection based on feedback control; according to the real-time traffic state data of each intersection, distributing a total traffic adjustment amount to each intersection to obtain a traffic adjustment amount expected value of each intersection; constructing an intersection signal control scheme optimization model considering bus priority by combining the traffic adjustment amount expected value of each intersection; and carrying out optimization solution on the intersection signal control scheme optimization model to obtain an optimized signal control scheme of each intersection. According to the invention, the signal control optimization scheme of each intersection of the regional boundary can be obtained, so that the bus priority is realized, and the regional traffic efficiency is improved.

Description

Technical Field

[0001] This application relates to the fields of public transportation, traffic signal control, and optimization decision-making, and specifically relates to an optimization method for signal control at regional boundary intersections considering bus priority. Background Art

[0002] With the acceleration of China's urbanization process and the continuous growth of the motor vehicle ownership, the problem of urban traffic congestion has become increasingly serious. Especially in the urban central area and main roads, traffic congestion has become the norm. At the same time, as an important part of the urban public transportation system, the operation efficiency of ground buses has also been seriously affected, resulting in a decrease in the on-time rate and attractiveness of buses. Most of the existing traffic signal control methods do not fully consider the differences between bus vehicles and social vehicles, and do not fully consider the priority traffic needs of bus vehicles. This not only violates the development concept of bus priority but also fails to give full play to the positive role of bus travel in alleviating traffic congestion. Therefore, how to design a scientific and reasonable signal control method for regional boundary intersections to ensure regional traffic efficiency while taking into account bus priority has become an urgent problem to be solved in the current traffic management field. Therefore, this application Summary of the Invention

[0003] The purpose of this application is to provide an optimization method for signal control at regional boundary intersections considering bus priority, which can improve the operation efficiency of urban traffic.

[0004] The technical solution provided by this application is as follows:

[0005] An optimization method for signal control at regional boundary intersections considering bus priority, comprising:

[0006] Step 1: Obtain the traffic detection data of the entire region;

[0007] Step 2: Construct a three-dimensional macroscopic fundamental Figure 3 D-MFD based on historical traffic detection data. The 3D-MFD is used to describe the relationship between the cumulative number of bus vehicles, the cumulative number of social vehicles, and the trip completion rate within the region;

[0008] Step 3: Taking the control cycle as a unit, at the beginning of each control cycle, obtain the actual value n b实 of the cumulative number of bus vehicles and the actual value n c实 of the cumulative number of social vehicles in the current region. Obtain the section corresponding to n b = n b实 from the 3D-MFD. In the section, obtain the n b实 value corresponding to the target trip completion rate that can be achieved under n c , denoted as Take as the critical value of the cumulative number of social vehicles; If Execute according to the original boundary intersection signal control plan; otherwise, go to step 4;

[0009] Step 4: Obtain the total traffic adjustment volume of each intersection according to the difference between n c实 and ;

[0010] Step 5: Allocate the total traffic adjustment volume to each intersection according to the real-time traffic status data of each intersection to obtain the expected traffic adjustment volume of each intersection;

[0011] Step 6: Combine the expected traffic adjustment volume of each intersection to construct an optimization model for the intersection signal control plan considering bus priority;

[0012] Step 7: Optimize and solve the optimization model of the intersection signal control plan to obtain the optimized signal control plan for each intersection.

[0013] In a possible implementation manner, in the said step 2, the basic form of the fitting function of the three-dimensional macroscopic fundamental diagram is as follows:

[0014] Collect traffic detection data according to vehicle types, eliminate abnormal points, obtain a three-dimensional macroscopic scatter plot based on historical traffic detection data, and use an exponential function to fit the three-dimensional macroscopic scatter plot, solve the coefficients of the exponential function, and obtain the three-dimensional macroscopic fundamental Figure 3 D-MFD of the area; the basic form of the exponential function is as follows:

[0015]

[0016] In the formula, G(n c , n b ) represents the trip completion rate corresponding to (n c , n b ), that is, the total number of private cars and buses that successfully complete trips in the area per unit time; n c represents the cumulative number of private cars in the area, n b represents the cumulative number of buses in the area, and a, b, c, d, e, f are all coefficients to be obtained.

[0017] In a possible implementation manner, in the said step 4, the proportional-derivative feedback control method is adopted. According to the difference between n c实 and , output the total traffic adjustment volume of each intersection, and the formula is:

[0018]

[0019] In the formula, represents The difference between the actual value and the critical value of the cumulative number of social vehicles within the time zone; The time is the start time of the kth control cycle and also the end time of the (k - 1)th control cycle; Represents the critical value of the cumulative number of social vehicles within the area; Represents The actual value of the cumulative number of social vehicles within the time zone; Represents the total traffic adjustment volume of each intersection in the kth control cycle; K p And K d Respectively represent the proportional adjustment coefficient and the differential adjustment coefficient.

[0020] In a possible implementation manner, in step 5, the expected value of the traffic adjustment volume of each intersection is calculated based on the following formula:

[0021]

[0022]

[0023] In the formula, i represents the intersection number, and I represents the set of intersection numbers; Represents the expected value of the traffic adjustment volume of the ith intersection in the kth control cycle;

[0024] In the formula, Represents the predicted value of the traffic adjustment demand of the ith intersection in the kth control cycle; ε k For calculating The intermediate variable; Represents the weight coefficient when allocating the total traffic adjustment volume to the ith intersection in the kth control cycle ;

[0025] In the formula, And Respectively represent the traffic demand for the ith intersection to drive out of the regional boundary and drive into the regional boundary in the kth control cycle; p represents the phase number; And Respectively represent the set of control signal phase numbers for controlling vehicles to drive into and out of the regional boundary of the ith intersection; LI i,p Represents the set of lane numbers of the pth phase of the ith intersection, and li is the lane number; Represents the number of queuing vehicles in the li lane of the pth phase of the ith intersection at the start time of the kth control cycle; Represents the vehicle arrival rate of the li lane of the pth phase of the ith intersection in the (k - 1)th control cycle; And Respectively represent the set of right-turn phase numbers for driving into the boundary and driving out of the regional boundary of the ith intersection; C represents the control cycle duration;

[0026] In the formula, and respectively represent the sum of the distances from the stop lines of all buses in all lanes of all phases at the beginning of the k-th control period for the i-th intersection when entering and leaving the regional boundary; α and β are the proportion coefficients considering the bus queue and the proportion coefficient considering the intersection traffic demand when calculating the distribution weight; j represents the queuing vehicle number; JB i,p,li represents the set of queuing bus position numbers in the li lane of the p-th phase at the i-th intersection; represents the distance between the j-th queuing vehicle in the lane of the p-th phase at the i-th intersection and the stop line at the k-th control period.

[0027] In a possible implementation manner, in step 6, for each intersection, with the goal of minimizing the change to the original signal control scheme, the phase sequence and green light duration in the signal control scheme are used as decision variables, and the control logic of the double-loop phase, the minimum green light duration constraint, the intersection traffic adjustment amount constraint, the maximum queue length constraint, and bus signal priority are considered to set the constraint conditions, and an optimization model for the intersection signal control scheme considering bus priority is constructed; among them, the intersection traffic adjustment amount constraint is set based on the expected value of the intersection traffic adjustment amount.

[0028] In a possible implementation manner, in step 6, the intersection traffic adjustment amount constraint is:

[0029]

[0030] In the formula, and respectively represent the traffic volumes when entering and leaving the regional boundary of the i-th intersection at the k-th control period under the current decision; represents the penalty coefficient given when the actual adjustment amount of the i-th intersection at the k-th control period does not reach the expected adjustment amount; represents the start time of the green light of the p-th phase at the i-th intersection at the k-th control period; represents the end time of the green light of the p-th phase at the i-th intersection at the k-th control period; represents the vehicle arrival rate of the li lane of the p-th phase at the i-th intersection at the k-th control period;

[0031] In the formula, represents the green light duration of the -th phase at the i-th intersection at the k-th control period, represents the phase sequence variable of the -th phase at the i-th intersection at the k-th control period; among them, the -th phase is the other phase in a phase pair with the p-th phase; bi Denote the boundary duration of the main road at the \(i\)-th intersection; Denote the green light duration of the \(p\)-th phase at the \(i\)-th intersection in the \(k\)-th control cycle;

[0032] The specific expression of the bus signal priority constraint is:

[0033]

[0034]

[0035] In the formula, and respectively denote the sum of the deduced waiting times of the buses queuing at the boundary of the entrance and exit areas of the \(i\)-th intersection in the \(k\)-th control cycle under the current decision passing through the stop line; and respectively denote the sum of the waiting times of the buses entering and exiting the \(i\)-th intersection in the \(k\)-th control cycle passing through the stop line deduced and calculated in combination with the actual signal control scheme; Denote the headway of the \(j\)-th vehicle in the \(l_i\) lane of the \(p\)-th phase at the \(i\)-th intersection in the \(k\)-th control cycle; \(wt\) i,p Denote the difference between the start time of the \(p\)-th phase cycle and the start time of the green light at the \(i\)-th intersection in the actual signal control scheme, which can be directly obtained according to the preset timing scheme of the actual signal control scheme;

[0036] In the formula, Denote the headway of the \(j\)-th vehicle queuing in the \(l_i\) lane of the \(p\)-th phase at the \(i\)-th intersection in the \(k\)-th control cycle; \(l1, l2, l3, l4, l5\) are start-up loss times, respectively representing the time taken for the first to the fifth vehicles in the queuing vehicles to start from a stop when they see the red light turn green; \(h\) denotes the saturated headway; Denote the headway calculated according to ;

[0037] On the second aspect, the present application provides an electronic device, including: a memory and a processor;

[0038] The memory is used to store a computer program;

[0039] The processor is used to call the computer program to execute the method as described above.

[0040] On the third aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program runs on an electronic device, the electronic device implements the method as described above.

[0041] Fourthly, the present application provides a computer program product, including a computer program which, when running on an electronic device, enables the electronic device to implement the method described above.

[0042] For the specific implementation manners of the second to fourth aspects of the present application, reference may be made to the implementation manner of the first aspect above, which will not be elaborated herein.

[0043] Beneficial effects:

[0044] The present application not only significantly improves the operation efficiency and punctuality rate of bus vehicles, but also reduces the delay of social vehicles through reasonable control and optimization of boundary signals, achieving a double improvement in bus priority and regional traffic efficiency. The present application has important practical significance for improving the urban traffic operation efficiency and promoting the development of public transportation. Description of the drawings

[0045] Figure 1 is a flowchart of the method according to an embodiment of the present application;

[0046] Figure 2 is a flowchart of the overall framework of the method according to an embodiment of the present application;

[0047] Figure 3 is a schematic diagram of a three-dimensional macroscopic fundamental diagram according to an embodiment of the present application;

[0048] Figure 4 is a schematic diagram of a section obtained under the cumulative number of a certain bus vehicle in the three-dimensional macroscopic fundamental diagram according to an embodiment of the present application;

[0049] Figure 5 is Figure 4 the curve graph corresponding to the section in;

[0050] Figure 6 is a schematic diagram of a standard 8-phase double-loop structure. Detailed implementation manners

[0051] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution of the present application will be further described in detail below in conjunction with the embodiments and drawings of the present application.

[0052] An embodiment of the present application proposes an optimization method for signal control of regional boundary intersections considering bus priority, which is used to optimize the signal control schemes of each intersection located on the regional boundary, aiming to achieve the dual goals of bus priority and improving regional traffic efficiency by dynamically adjusting the intersection signal control schemes. The intersections in this application all refer to the intersections located on the regional boundary. This method first constructs a three-dimensional macroscopic fundamental diagram (3D-MFD) based on the historical traffic detection data in the region to describe the relationship between the cumulative number of bus vehicles, the cumulative number of social vehicles, and the trip completion rate (i.e., the number of vehicles that successfully complete trips in the region per unit time) in the region. Then, at the beginning of the control cycle of the intersection, the cumulative number of bus vehicles and the cumulative number of social vehicles in the region at this time are detected, and based on the obtained actual data, the critical value of the cumulative number of social vehicles that can achieve the maximum trip completion rate under the current actual cumulative number of bus vehicles is obtained from the 3D-MFD. It is judged whether the actual value of the cumulative number of social vehicles in the current region is greater than the critical value of the cumulative number of social vehicles. If it is not greater than the critical value, it means that the region is not congested and boundary control is not required, and the original signal control scheme is executed. Otherwise, the following operations are required: Determine the expected total traffic adjustment amount (i.e., the difference between the actual value of the cumulative number of social vehicles and the critical value) based on the critical value provided by the 3D-MFD, and use the proportional-derivative (PD) feedback control method to obtain the total traffic adjustment amount of the intersection (i.e., the difference between the number of vehicles leaving and entering the regional boundary). The total traffic adjustment amount is allocated to each intersection according to the real-time traffic detection data of each intersection (such as lane queue length, vehicle arrival rate, bus vehicle position, etc.). Then, an optimization model for the intersection signal control scheme considering bus priority is constructed for each intersection. With the goal of minimizing the change to the original signal control scheme, the phase sequence and green light duration in the signal control scheme are used as decision variables, and constraints are set considering the control logic of the double-ring phase, the traffic safety of vehicles and pedestrians in the intersection, the number of vehicles leaving and entering the regional boundary meeting the expectations, not sacrificing the traffic state outside the region, and not sacrificing the waiting or delay time of buses. A mature solver is used to solve it efficiently to obtain the optimal signal control scheme, which is input into the traffic signal control system for execution. This method aims to improve the overall traffic efficiency of the region while realizing bus priority. This application not only significantly improves the operation efficiency and punctuality rate of bus vehicles, but also reduces the delay of social vehicles through reasonable signal control optimization, achieving the dual improvement of bus priority and regional traffic efficiency.

[0053] The following will describe the specific embodiments according to the present application with reference to the accompanying drawings.

[0054] Embodiment 1:

[0055] As Figure 1As shown in the figure, an optimization method for signal control at regional boundary intersections considering bus priority is proposed in an embodiment of the present application, including:

[0056] Step 1: Obtain traffic detection data for the entire region.

[0057] As Figure 2 shown, multi-source heterogeneous traffic detection data (vehicle-level data, regional-level data, and infrastructure data) covering the entire region can be obtained based on intelligent data detection equipment such as the Global Positioning System (GPS), Inertial Measurement Unit (IMU), road sensors, and video surveillance systems, including key information such as bus positions, average traffic flow, average driving speed, average trip length, cumulative number of vehicles, and queue length, including all available data to be used in subsequent steps.

[0058] These data are transmitted to the traffic management center through wireless communication technology for real-time analysis and processing, providing data support for subsequent signal control optimization.

[0059] Step 2: Construct a three-dimensional macroscopic fundamental diagram (3D-MFD) based on historical traffic detection data, which is used to describe the relationship between the cumulative number of bus vehicles, the cumulative number of social vehicles, and the trip completion rate within the region.

[0060] A schematic diagram of the three-dimensional macroscopic fundamental diagram is as Figure 3 shown.

[0061] The macroscopic fundamental diagram regards all vehicles as a unified traffic flow, while the real urban road network is a complex system where multiple traffic modes interact with each other. The macroscopic fundamental diagram under the dual-mode of the road network (referring to buses and ordinary cars) is called the three-dimensional macroscopic fundamental diagram.

[0062] As an important part of the method of the present application, the construction of the three-dimensional macroscopic fundamental diagram (3D-MFD) in step 2 is specifically as follows:

[0063] In some embodiments, traffic detection data is collected according to vehicle types, abnormal points are removed, a three-dimensional macroscopic scatter diagram based on historical traffic detection data is obtained, and an exponential function is used to fit the three-dimensional macroscopic scatter diagram. The coefficients of the exponential function are solved, and the three-dimensional macroscopic fundamental diagram of this region is fitted to provide a theoretical basis for the optimization of signal control parameters.

[0064] In some embodiments, the particle swarm optimization algorithm (PSO) can be used to solve the coefficients of the exponential function.

[0065] Relating the total vehicle traffic flow to the cumulative amounts of social vehicles and buses, a 3D-MFD considering traffic flow is proposed, and the relationship between them is fitted using an exponential function. The specific calculation expression of the trip completion rate is as follows:

[0066]

[0067] Wherein, G(n(t)) represents the travel completion rate corresponding to n(t); L represents the average travel length, that is, the average driving distance of the vehicle from the starting point to the end point; L N represents the total length of all road sections in the area, that is, the sum of the lengths of each road from the starting point to the end point; q(t) represents the average flow in the area per unit time t, that is, the total number of vehicles passing through all cross-sections in the area in unit time t; n(t) represents the cumulative number of vehicles in the area in unit time t; V(n(t)) represents the average driving speed of n(t) vehicles in the area.

[0068] The calculation expression of the average flow of vehicles in the area is as follows:

[0069] q(k c ,k b ) = q c (k c ,k b ) + q b (k c ,k b )

[0070] Wherein, q(k c ,k b ) represents the average flow of all vehicles in the area, q c (k c ,k b ) and q b (k c ,k b ) respectively represent the average flow of social vehicles and buses; k c and k b respectively represent the average density of social vehicles and the average density of buses.

[0071] Among them, the specific calculation formula for the average flow of the m-th type of vehicle is:

[0072]

[0073] Wherein, l u represents the length of the u-th road section in the area; L represents the average length of the road sections in the area; q um represents the flow of the m-th type of vehicle on the u-th road section, where m = 1, 2 correspond to social vehicles and buses respectively.

[0074] Referring to the above formula, collecting traffic detection data according to vehicle types and removing abnormal points, a three-dimensional macroscopic scatter plot based on historical traffic detection data can be obtained.

[0075] As an important part of the method of this application, the three-dimensional macroscopic fundamental diagram (3D-MFD) in step 2 fits the coefficients of the exponential function through the particle swarm optimization (PSO). The specific process is as follows:

[0076] The basic form of the exponential function is as follows:

[0077]

[0078] In the formula, G(n c ,n b ) represents the trip completion rate corresponding to (n c ,n b ), that is, the total number of social vehicles and buses that have successfully completed trips in the area per unit time, which is the Z-axis in the three-dimensional macroscopic fundamental diagram; n c represents the cumulative number of social vehicles in the area per unit time, and n b represents the cumulative number of bus vehicles in the area per unit time. a, b, c, d, e, and f are all coefficients to be obtained.

[0079] Use the particle swarm optimization algorithm to solve the coefficients a, b, c, d, e, and f in the formula. The optimization function for solving the coefficients is defined as the following formula. The objective function represents that the root mean square error between the predicted trip completion rate value and the true trip completion rate value is minimized. The calculation method is as follows:

[0080]

[0081] In the formula, v represents the data point number; N represents the total number of data points; Q pred,v represents the trip completion rate corresponding to n c =n c,v and n b =n b,v calculated using the exponential function; Q true,v represents the true trip completion rate obtained through statistics.

[0082] Step 3: Taking the control period as a unit, at the start moment of each control period, obtain the actual value n b实 of the cumulative number of bus vehicles and the actual value n c实 of the cumulative number of social vehicles in the current area. Obtain the section corresponding to n b =n b实 from the 3D-MFD. In the section, obtain the n b实 value corresponding to the target trip completion rate that can be achieved under n c , denoted as Take as the critical value of the cumulative number of social vehicles; if Execute according to the original boundary intersection signal control plan; otherwise, go to step 4.

[0083] In some embodiments, the target trip completion rate takes the maximum trip completion rate.

[0084] This step is based on the current traffic detection data and combines 3D-MFD to determine whether regional boundary intersection signal optimization control is required. Specifically, at the beginning of each control cycle, obtain the actual value n of the cumulative number of bus vehicles in the current area b实 and the actual value n of the cumulative number of social vehicles c实 , as Figure 4 and Figure 5 shown, obtain n from the constructed three-dimensional macroscopic fundamental Figure 3 D-MFD b = the actual value n of the cumulative number of bus vehicles in the current area b实 corresponding section, and obtain the cumulative number n of social vehicles corresponding to the maximum trip completion rate that can be achieved under the actual value n of the cumulative number of bus vehicles in the current area b实 in the section, and use this value as the critical value of the cumulative number of social vehicles c Judge whether the actual value n of the cumulative number of social vehicles in the current area is greater than the critical value of the cumulative number of social vehicles c实 If it indicates that the traffic state in the area has not reached congestion, and boundary intersection signal optimization control is not required. Execute according to the original boundary intersection signal control plan; conversely, if then boundary intersection signal optimization control is required. If boundary intersection signal optimization control is required, proceed to the subsequent steps. Step 4: Obtain the total traffic adjustment volume of each intersection according to the difference between n

[0085] and c实 ; The traffic adjustment volume is the difference between the number of vehicles leaving the regional boundary and the number of vehicles entering the regional boundary.

[0086] In some embodiments, based on the critical value provided by 3D-MFD, the difference between the actual value of the cumulative number of social vehicles and the critical value is used as the desired total traffic adjustment volume; the proportional-derivative (PD) feedback control method (PD controller) is adopted. According to the difference between the actual value n of the cumulative number of social vehicles in the current area

[0087] and the critical value c实 of the cumulative number of social vehicles , output the total traffic adjustment volume of each intersection, and dynamically control the cumulative number of social vehicles in the area to be maintained near the critical value, so that the total trip completion rate in the area reaches the target trip completion rate (such as the maximum trip completion rate).

[0088] The formula for calculating the total traffic adjustment volume of each intersection is as follows:

[0089]

[0090] In the formula, represents the difference between the actual value and the critical value of the cumulative number of social vehicles in the time zone; this time is the start time of the k-th control cycle and also the end time of the (k - 1)-th control cycle; represents the critical value of the cumulative number of social vehicles in the area; represents the actual value of the cumulative number of social vehicles in the time zone. represents the total traffic adjustment volume of each intersection in the k-th control cycle, and this value is calculated by combining the traffic detection data obtained at time under the premise of meeting the control conditions; K p and K d represent the proportional adjustment coefficient and the differential adjustment coefficient respectively.

[0091] Step 5: According to the real-time traffic state data of each intersection, allocate the total traffic adjustment volume to each intersection.

[0092] The traffic state data is the data describing the real-time operation characteristics of a specific intersection, such as the lane queue length, vehicle arrival rate, and vehicle position under different phases and different lanes of different intersections.

[0093] In the allocation of the total traffic adjustment volume it can be understood as the sum of the expected values of the traffic adjustment volumes of each intersection, and the specific formula is as follows:

[0094]

[0095] In the formula, i represents the intersection number, I represents the set of intersection numbers; k represents the control cycle number; represents the expected value of the traffic adjustment volume of the i-th intersection in the k-th control cycle.

[0096] The expected value of the traffic adjustment volume of each intersection can be expressed by combining the traffic adjustment demand prediction value with the weight coefficient:

[0097]

[0098] In the formula, represents the predicted value of the traffic adjustment demand (the difference between the traffic demand for driving out of the regional boundary and the traffic demand for driving into the regional boundary) of the i-th intersection in the k-th control cycle; ε k is for calculation The intermediate variable has no specific meaning; represents the total traffic adjustment amount allocated to the i-th intersection in the k-th control period at the time of the weight coefficient.

[0099] Among them, The calculation formula expression is:

[0100]

[0101] In the formula, and respectively represent the traffic demand at the boundary of the departure area and the boundary of the entry area of the i-th intersection in the k-th control period; p represents the phase number; and respectively represent the sets of control signal phase numbers for controlling vehicles to enter and exit the boundary of the area of the i-th intersection; LI i,p represents the set of lane numbers of the p-th phase of the i-th intersection, and li is the lane number; represents the number of queuing vehicles in the li lane of the p-th phase of the i-th intersection at the start time of the k-th control period; represents the vehicle arrival rate in the li lane of the p-th phase of the i-th intersection in the (k - 1)-th control period; and respectively represent the sets of right-turn phase numbers at the boundary of the entry area and the boundary of the departure area of the i-th intersection; C represents the control period duration.

[0102] Among them, the standard 8-phase double-loop structure is as Figure 6 shown. Exemplarily, for a cross intersection controlled by an 8-phase double-loop structure signal, there are 8 signal control phases. Among them, phase 1 corresponds to the left-turn phase of the east approach; phase 2 corresponds to the straight-through phase of the west approach; phase 3 corresponds to the left-turn phase of the north approach; phase 4 corresponds to the straight-through phase of the south approach; phase 5 corresponds to the left-turn phase of the west approach; phase 6 corresponds to the straight-through phase of the east approach; phase 7 corresponds to the left-turn phase of the south approach; phase 8 corresponds to the straight-through phase of the north approach. The right-turn phases of each approach are non-signal control phases. The 8 signal control phases form 4 phase pairs, and each phase pair has 2 phase sequences, for a total of 16 phase sequence combination schemes, as Figure 6 shown, where phase 1 and 2 are a phase pair, phase 3 and 4 are a phase pair, phase 5 and 6 are a phase pair, and phase 7 and 8 are a phase pair. The release order of the two phases within a phase pair can be changed. For example, for phase 1 and 2, 1 can be released first and then 2, or 2 can be released first and then 1. The specific phases at the boundaries of the entry and departure areas are determined according to the orientation of the intersection and the division of the area.

[0103] Weight coefficient The calculation mainly considers two aspects: bus priority and traffic demand. The specific expression is as follows:

[0104]

[0105]

[0106] In the formula, and respectively represent the sum of the distances from the stop lines of all buses in all lanes of all phases entering and leaving the area boundary of the i-th intersection at the start time of the k-th control period; α and β are the proportion coefficients considering the bus queue and the proportion coefficient considering the traffic demand at the intersection when calculating the distribution weight, and empirical values can be taken; j represents the queuing vehicle number (the queuing vehicles on the same lane are numbered sequentially from 1 in ascending order of the distance from the stop line to obtain the numbers of each queuing vehicle); JB i,p,li represents the set of queuing bus position numbers in the li lane of the p-th phase at the i-th intersection (forming a set of the position numbers of bus vehicles); represents the distance between the j-th queuing vehicle in the li lane of the p-th phase at the i-th intersection in the k-th control period and the stop line.

[0107] Step 6: Combine the expected values of traffic adjustment amounts at each intersection to construct an optimization model for the intersection signal control scheme considering bus priority.

[0108] For each intersection, with the goal of minimizing the change to the original signal control scheme, taking the phase sequence and green light duration in the signal control scheme as decision variables, considering the control logic of double-ring phases, the minimum green light duration constraint, the intersection traffic adjustment amount constraint, the maximum queue length constraint, and bus signal priority to set constraint conditions, an optimization model for the intersection signal control scheme considering bus priority is constructed.

[0109] The decision variables are: including two parts, namely the phase sequence and the green light duration (duration) in the optimized signal control scheme. Among them, the phase sequence is described by the 0-1 variable where represents the phase sequence variable of the p-th phase at the i-th intersection in the k-th control period; means that the p-th phase at the i-th intersection in the k-th control period is released before the -th phase, means that the p-th phase at the i-th intersection in the k-th control period is released after the -th phase, where the -th phase is the other phase in a phase pair with the p-th phase. The green light duration is represented by the continuous variable where p ∈ {1, 2,..., 8}, Denote the green light duration of the $p$-th phase at the $i$-th intersection in the $k$-th control period;

[0110] The objective function is:

[0111]

[0112] In the formula, Denote the green light duration of the $p$-th phase at the $i$-th intersection in the $k$-th control period in the original signal control scheme; Denote the penalty coefficient given when the actual adjustment amount at the $i$-th intersection in the $k$-th control period fails to reach the expected adjustment amount.

[0113] Constraint conditions:

[0114] (1) Control logic constraint for double-ring phases:

[0115]

[0116] In the formula, Denote the green light duration of the $p$-th phase at the $i$-th intersection in the $k$-th control period; $y$ represents the yellow light time, taking a fixed value of 3 s; $b$ Denote the boundary duration of the main road at the $i$-th intersection (i.e., the duration of each signal control phase of the main road); $C$ represents the control period duration. i Denote the boundary duration of the main road at the $i$-th intersection (i.e., the duration of each signal control phase of the main road); $C$ represents the control period duration.

[0117]

[0118] In the formula, Then denote the phase sequence variable of the $p$-th phase at the $i$-th intersection in the $k$-th control period. Denote the phase sequence variable of the $p$-th phase at the $i$-th intersection in the $k$-th control period.

[0119] (2) Minimum green light duration constraint:

[0120]

[0121] In the formula, $g$ min Denote the minimum green light duration, taking a fixed value of 15 s.

[0122] Ensure the traffic safety of vehicles and pedestrians in the intersection through the minimum green light duration constraint.

[0123] (3) Intersection traffic adjustment amount constraint:

[0124]

[0125] In the formula, And Denote the traffic volume entering and leaving the boundary of the $i$-th intersection in the $k$-th control period under the current decision respectively; Denote the start time of the green light for the p-th phase at the i-th intersection in the k-th control cycle; Denote the end time of the green light for the p-th phase at the i-th intersection in the k-th control cycle; Denote the vehicle arrival rate of the li lane for the p-th phase at the i-th intersection in the k-th control cycle.

[0126] Ensure that the number of vehicles exiting and entering the regional boundary meets the expectation through the constraint of intersection traffic adjustment volume.

[0127] (4) Maximum queue length constraint:

[0128]

[0129] In the formula, Denote the sum of the maximum number of queuing vehicles for each phase used to control the vehicle entry into the regional boundary at the i-th intersection in the k-th control cycle; Denote the sum of the maximum queue lengths for each phase at the entry boundary of the i-th intersection; Denote the average headway of the queue, taking a fixed value of 1.8m; L c Denote the vehicle length of a single social vehicle, taking a fixed value of 5m; LE i,p.li Denote the length of the li lane (distance between adjacent intersections) for the p-th phase at the i-th intersection.

[0130] Ensure that the traffic conditions outside the region are not sacrificed through the maximum queue length constraint.

[0131] (5) Bus signal priority constraint:

[0132] The expression of the headway calculation formula is:

[0133]

[0134] In the formula, Denote the headway of the j-th vehicle queuing in the li lane for the p-th phase at the i-th intersection in the k-th control cycle; l1, l2, l3, l4, l5 are start-up loss times, respectively representing the time taken for the first to the fifth vehicles in the queuing vehicles to start from a stop when they see the red light turn green; l1, l2, l3, l4, l5 are empirical values, and in some embodiments, they can take fixed values of 1.4s, 0.8s, 0.5s, 0.3s, 0.1s respectively; h represents the saturated headway, which is an empirical value and can take a fixed value of 2.2s; Denote according to The calculated headway (i.e., the reciprocal of the vehicle arrival rate).

[0135] The specific expression of the bus signal priority constraint is:

[0136]

[0137]

[0138] In the formula, and respectively represent the sum of the deduced waiting time values of the queuing buses passing the stop line in the in - going and out - going area boundaries at the i - th intersection in the k - th control period under the current decision; and respectively represent the sum of the waiting times of the in - going and out - going buses passing the stop line at the i - th intersection in the k - th control period deduced and calculated in combination with the actual signal control scheme; represents the headway of the j - th vehicle in the li lane of the p - th phase at the i - th intersection in the k - th control period; wt i,p represents the difference between the start time of the p - th phase cycle and the start time of the green light at the i - th intersection in the actual signal control scheme, which can be directly obtained according to the preset timing plan of the actual signal control scheme.

[0139] Ensure that the waiting or delay time of buses is not sacrificed through bus signal priority constraints.

[0140] By setting the above - mentioned constraint conditions, it can be ensured that the optimized intersection signal control scheme obtained by solving satisfies the control logic of the double - loop phase, the traffic safety of vehicles and pedestrians within the intersection, the number of vehicles at the in - going and out - going area boundaries meets the expectations, the traffic conditions outside the area are not sacrificed, and the waiting or delay time of buses is not sacrificed.

[0141] Step 7: Optimize and solve the intersection signal control scheme optimization model to obtain the signal control schemes of each optimized intersection.

[0142] Respectively use the mature mathematical programming solver Gurobi to solve the optimized signal control scheme of the corresponding intersection in the current control period, and obtain the signal control schemes of each optimized intersection, where the solution variables include the adjustment of the phase sequence and the green - light duration.

[0143] By dynamically adjusting the phase sequence and the green - light duration in the signal control scheme of each intersection, a signal control scheme that realizes bus priority can be obtained under the goal of ensuring the maximum regional traffic efficiency.

[0144] In some embodiments, the method further includes:

[0145] Step 8: Input the signal control schemes of each optimized intersection into the traffic signal control system and execute them.

[0146] Inputting the optimized signal control schemes of each intersection into the traffic signal control system and executing them can achieve the coordinated control of buses and social vehicles, optimize the signal timing at the regional boundary, improve the operation efficiency and punctuality rate of the bus system, ensure the smooth passage of social vehicles at the same time, and enhance the overall traffic efficiency of the region.

[0147] Embodiment 2:

[0148] This embodiment provides an electronic device, including: a memory and a processor;

[0149] The memory is used to store a computer program;

[0150] The processor is used to call the computer program to execute the method as described in Embodiment 1.

[0151] Embodiment 3:

[0152] This embodiment provides a computer-readable storage medium, in which a computer program is stored. When the computer program runs on an electronic device, the electronic device is enabled to implement the method as described in Embodiment 1.

[0153] Embodiment 4:

[0154] This embodiment provides a computer program product, including a computer program. When the computer program runs on an electronic device, the electronic device is enabled to implement the method as described in Embodiment 1.

[0155] The specific implementation manners of a system, an electronic device, a computer-readable storage medium, and a computer program product provided in the embodiments of the present application may refer to the specific embodiments of the above method, and will not be elaborated herein.

[0156] Obviously, those skilled in the art should understand that the above units or steps of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to implement. In this way, the present application is not limited to any specific combination of hardware and software.

[0157] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An optimization method for signal control at regional boundary intersections considering bus priority, characterized in that, Including: Step 1: Obtain traffic detection data for the entire area; Step 2: Construct a three-dimensional macroscopic fundamental diagram 3D-MFD based on historical traffic detection data. The 3D-MFD is used to describe the relationship between the cumulative number of bus vehicles, the cumulative number of social vehicles, and the trip completion rate within the area; Step 3: Taking the control cycle as a unit, at the beginning of each control cycle, obtain the actual value n of the cumulative number of buses in the current area b实 and the actual value of the cumulative number of social vehicles n c实 , get n from 3D-MFD b =n b实 The corresponding section, in the section, obtains b实 The target trip completion rate that can be achieved under n c Value, denoted as Will As the critical value of the cumulative number of social vehicles; Execute according to the original boundary intersection signal control plan; otherwise, go to step 4; Step 4: Obtain the total traffic adjustment volume of each intersection according to the difference between n c实 and ; Step 5: According to the real-time traffic status data of each intersection, allocate the total traffic adjustment amount to each intersection to obtain the expected value of the traffic adjustment amount for each intersection; Step 6: Combine the expected values of the traffic adjustment amounts of each intersection to construct an optimization model for the intersection signal control plan considering bus priority; Step 7: Optimize and solve the optimization model for the intersection signal control plan to obtain the optimized signal control plan for each intersection.

2. The method according to claim 1, wherein In the said Step 2, the basic form of the fitting function of the three-dimensional macroscopic fundamental diagram is as follows: Collect traffic detection data according to vehicle types, eliminate abnormal points, obtain a three-dimensional macroscopic scatter diagram based on historical traffic detection data, and perform function fitting on the three-dimensional macroscopic scatter diagram using an exponential function, solve the coefficients of the exponential function to obtain the three-dimensional macroscopic fundamental diagram 3D-MFD of the said area; the basic form of the exponential function is as follows: Wherein, G(n c ,n b ) represents the trip completion rate corresponding to (n c ,n b ), that is, the total number of social vehicles and buses that have successfully completed trips in the area per unit time; n c represents the cumulative number of social vehicles in the area, and n b represents the cumulative number of bus vehicles in the area. a, b, c, d, e, and f are all coefficients to be obtained.

3. The method according to claim 1, wherein In the step 4, a proportional-derivative feedback control method is adopted, and according to the difference between n c实 and , the total traffic adjustment amount of each intersection is output, and the formula is: In the formula, represents the difference between the actual value and the critical value of the cumulative number of social vehicles in the time zone; The time is the start time of the kth control cycle and also the end time of the (k - 1)th control cycle; represents the critical value of the cumulative number of social vehicles in the area; represents the actual value of the cumulative number of social vehicles in the time zone; represents the total traffic adjustment amount of each intersection in the kth control cycle; K p and K d represent the proportional adjustment coefficient and the differential adjustment coefficient respectively.

4. The method according to claim 3, wherein In the said Step 5, calculate the expected value of the traffic adjustment amount for each intersection based on the following formula: Where \(i\) represents the intersection number, and \(I\) represents the set of intersection numbers; represents the expected value of the traffic adjustment volume of the \(i\)-th intersection in the \(k\)-th control cycle; In the formula, represents the predicted value of the traffic adjustment demand at the i-th intersection in the k-th control cycle; ε k For calculating intermediate variable; Denote the weight coefficient when allocating the total traffic adjustment volume of the i-th intersection in the k-th control period ; In the formula, and respectively represent the traffic demand at the boundary of the outgoing area and the incoming area of the i-th intersection in the k-th control period; p represents the phase number; and respectively represent the sets of control signal phase numbers for controlling the entry and exit of vehicles at the boundary of the i-th intersection; LI i,p represents the set of lane numbers of the p-th phase of the i-th intersection, and li is the lane number; represents the number of queuing vehicles in the li lane of the p-th phase of the i-th intersection at the start of the k-th control period; represents the vehicle arrival rate in the li lane of the p-th phase of the i-th intersection in the (k - 1)-th control period; and respectively represent the sets of right-turn phase numbers at the incoming boundary and the right-turn phase numbers at the outgoing area boundary of the i-th intersection; C represents the control period duration; Wherein, and respectively represent the sum of the distances from the stop lines of all buses in all lanes of all phases entering and leaving the regional boundary of the i-th intersection at the start time of the k-th control period; α and β are the proportion coefficients considering the bus queue and the proportion coefficient considering the traffic demand at the intersection when calculating the distribution weight; j represents the queue vehicle number; JB i,p,li represents the set of queue bus position numbers in the li lane of the p-th phase of the i-th intersection; represents the distance between the j-th queuing vehicle in the lane of the p-th phase of the i-th intersection and the stop line in the k-th control period.

5. The method according to claim 4, characterized in that, In the said Step 6, for each intersection, with the goal of minimizing the change to the original signal control plan, use the phase sequence and green light duration in the signal control plan as decision variables, consider the control logic of the double-ring phase, the minimum green light duration constraint, the intersection traffic adjustment amount constraint, the maximum queue length constraint, and bus signal priority to set constraint conditions, and construct an optimization model for the intersection signal control plan considering bus priority; among them, the intersection traffic adjustment amount constraint is set based on the expected value of the traffic adjustment amount of the intersection.

6. The method according to claim 5, wherein In the said Step 6, the intersection traffic adjustment amount constraint is: Wherein, and respectively represent the traffic volumes entering and leaving the regional boundary of the ith intersection in the kth control cycle under the current decision; represents the penalty coefficient given when the actual adjustment amount of the ith intersection in the kth control cycle fails to reach the expected adjustment amount; represents the start time of the green light of the pth phase of the ith intersection in the kth control cycle; represents the end time of the green light of the pth phase of the ith intersection in the kth control cycle; represents the vehicle arrival rate of the li lane of the pth phase of the ith intersection in the kth control cycle; In the formula, represents the green light duration of the -th phase of the i-th intersection in the k-th control cycle, represents the phase sequence variable of the -th phase of the i-th intersection in the k-th control cycle; where the -th phase is the other phase in a phase pair with the p-th phase; b i represents the boundary duration of the main road of the i-th intersection; represents the green light duration of the p-th phase of the i-th intersection in the k-th control cycle; The specific expression of the bus signal priority constraint is: Wherein, and respectively represent the sum of the deduced waiting time values of the buses queuing at the stop line in the in - driving and out - driving area boundaries of the \(i\) - th intersection in the \(k\) - th control period under the current decision; and respectively represent the sum of the waiting times of the in - driving and out - driving buses passing the stop line at the \(i\) - th intersection in the \(k\) - th control period deduced and calculated in combination with the actual signal control scheme; represents the headway of the \(j\) - th vehicle in the \(l_i\) lane of the \(p\) - th phase at the \(i\) - th intersection in the \(k\) - th control period; wt i,p represents the difference between the start time of the \(p\) - th phase cycle and the start time of the green light at the \(i\) - th intersection in the actual signal control scheme, which can be directly obtained according to the preset timing scheme of the actual signal control scheme; In the formula, represents the headway of the j-th vehicle queued in the li lane of the p-th phase of the i-th intersection in the k-th control period; l1, l2, l3, l4, l5 are start-up loss times, representing the time taken for the 1st to 5th vehicles in the queued vehicles to start from a stop after seeing the red light turn green, respectively. h represents the saturated headway; represents the headway calculated according to the headway obtained by calculation.

7. The method according to claim 1, characterized in that, The said method further includes: Step 8: Input the optimized signal control plan for each intersection into the traffic signal control system and execute it.

8. An electronic device, characterized in that, Including: A memory and a processor; The said memory is used to store computer programs; The said processor is used to call the said computer program to execute the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program runs on an electronic device, the electronic device realizes the method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program runs on an electronic device, the electronic device realizes the method as described in any one of claims 1 to 7.

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