A Method for Scheduling Right-of-Way at Intersections with Demand Collaboration

Abstract

The present invention discloses a method for scheduling the right of way at intersections with demand coordination. The method includes: obtaining basic data such as the spatial layout of the urban road network, roadside detection devices, vehicle and pedestrian distributions, and calculating and predicting the time and probability of traffic travel subjects in the road network arriving at each intersection according to data such as OD data, detection data, road network structure, and historical trajectories; obtaining the total time-series distribution of the passing demand at the intersection in a future period of time and constructing a time-series passing demand map; taking the total passing demand C on the demand map as the control target, preferentially scheduling the phases with strong passing demand, and calculating and selecting the phase extension time Δt according to the change of ΔC to achieve the active scheduling of the right of way at the intersection and improve the passing efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic control, and particularly relates to a method for scheduling the right of way at intersections with demand coordination. Background Art

[0002] As a key node in the urban road network, intersections play a role in scheduling traffic flows and allocating the right of way. Urban roads are composed of various traffic participants such as pedestrians, motor vehicles (private cars, buses, 110 / 119 / 120, etc.), and non-motor vehicles. There are many differences in aspects such as driving speed and strength of traffic demand. These differentiated traffic demands converge at intersections. When the intersections cannot meet the traffic demands in time, traffic congestion may occur and efficiency may decrease.

[0003] Traditionally, in traffic management, traffic demands are identified through two methods: manual statistics and intelligent perception, and the phase and green time of the phase are adjusted to schedule the right of way. However, intelligent perception has problems of low proportion and low accuracy, and cannot accurately grasp traffic demands. Therefore, only when pedestrians, non-motor vehicles, and motor vehicles approach the intersection can the traffic demands be confirmed and passive adjustments be made. This results in a small adjustment space, slow response, and low efficiency.

[0004] With the advancement and popularization of intelligent networking, autonomous driving, intelligent roadside perception, customized buses, and autonomous navigation, traffic demands are becoming more complex. Only by accurately perceiving and predicting traffic demands through intelligent and diverse data perception means, coordinating the traffic demands at intersections, and making active responses by scheduling the phase and green time of the phase can personalized traffic demands be met and the traffic efficiency at intersections be improved. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for scheduling the right of way at intersections with demand coordination to achieve coordinated scheduling of traffic demands at intersections to a certain extent in view of the problems existing in the prior art.

[0006] The technical solution for achieving the purpose of the present invention is as follows: A method for scheduling the right of way at intersections with demand coordination. First, a representation method for traffic demands at intersections is constructed to store the traffic demands of traffic travel subjects such as pedestrians, non-motor vehicles, and motor vehicles at intersections, forming a time-series traffic demand map. Secondly, by statistically analyzing historical data, the variation law of traffic demands at intersections and the threshold range of traffic demands at intersections are mastered. Finally, according to the time-series traffic demand map, coordinated responses are made to the traffic demands of traffic travel subjects such as pedestrians, non-motor vehicles, and motor vehicles, the strength of intersection demands is dynamically evaluated, the green light duration of phases is allocated in real time, and the right of way is scheduled.

[0007] Furthermore, a data structure for the traffic demand at intersections is provided, which stores the traffic demand of traffic participants such as pedestrians, non-motor vehicles, and motor vehicles. The data structure fields include identity identification, entry direction, exit direction, passing lane, demand level, demand probability, and arrival time. Among them, the identity identification is globally unique.

[0008] Furthermore, a method for generating a time-sequential traffic demand map at intersections is provided. The directed traffic flow from a certain entry direction to an exit direction at an intersection forms a directed passing chain. One phase corresponds to multiple passing chains, and the superposition of multiple passing chains constitutes the traffic demand map at the intersection. The calculation formula for the traffic demand of each traffic participant on the passing chain is: D i = α i * P i * w i * t i , where α i is the probability adjustment coefficient, with a value range of [0.5, 1]. The value is smaller the farther away from the intersection, and the value is 1 when stopped in front of the intersection; P i is the probability of arriving at the stop line of a certain entry lane at the intersection at a certain moment, w i is the level of traffic demand, and t i is the duration from the moment of arriving at the intersection until the traffic demand response; the calculation formula for the traffic demand on the passing chain is: W j is the traffic demand on passing chain j; the calculation formula for the traffic demand on the phase is: The calculation formula for the total traffic demand at the intersection is:

[0009] Furthermore, a method for calculating the traffic demand probability is provided. First, obtain information such as the positions, driving directions, speeds, and downstream intersections of traffic participants such as pedestrians, non-motor vehicles, and motor vehicles in the road network; second, obtain the historical travel time data set. According to the position data, the current time period, and the traffic state, use the clustering method to screen out the travel time data sets T of pedestrians, non-motor vehicles, and motor vehicles under the same traffic background from the historical data; third, according to the individual characteristics, screen out the historical travel time sample data sets T' of the same type in the travel time data set, and screen out the maximum travel time t ,max and the minimum travel time t min , with Δt as the sliding window interval, calculate the interval distribution probability to obtain the probability set P = [P 0 , P 1 , P 2 ,..., P m ; third, calculate the cumulative probability of this data, and use the travel time t 85As the arrival time and probability of the passing demand; finally, according to the running position, dynamically adjust the arrival time and probability of the passing demand of the vehicle until it reaches the intersection and confirm the demand.

[0010] Furthermore, a method for correcting the total passing demand at an intersection is provided. According to the position data of the stopped vehicle, calculate the distance from the stop line, estimate the number of vehicles stopped in the section from the stop position to the stop line, and thus estimate the demand W of the passing chain at this position. L , and compare it with the demand W at the corresponding lane and position in the passing demand diagram of the intersection. 0 The error ΔW = |W L -W 0 |. If ΔW exceeds the threshold, then correct W 0 , and synchronously correct the phase and the passing demand at the intersection.

[0011] Furthermore, a method for scheduling the passing right at an intersection is provided. The method includes: taking the total passing demand C at the intersection as the control target; calculating the passing demand of the released phase at the intersection, and the phase with stronger demand obtains the passing right first; dividing the green light time of the phase into two parts, fixed green light and extended green light. The fixed green light duration is obtained by weighted calculation according to the confirmed demand in the passing demand diagram of the intersection and the extended green light duration in the previous release; the extended green light duration is confirmed according to the change of ΔC at the intersection to extend or switch the passing right.

[0012] Furthermore, a method for switching the passing right is provided. At the end moment of the green light period, calculate the expected extension time Δt, and the value range is [Δt min -Δt max ; Δt min、 Δt max is confirmed according to factors such as traffic travel subjects, intersection space shape, conflict points, etc.; within the range of [Δt min -Δt max , calculate ΔC second by second, and take its maximum value ΔC max . If ΔC max > 0, then take the Δt max corresponding to ΔC i as the extended phase time; if ΔC max ≤ 0, then this phase ends and the passing right transitions to the next phase.

[0013] Compared with the prior art, the significant advantages of the present invention are:

[0014] 1) The present invention digitally expresses the passing demands of traffic travel subjects such as pedestrians, non-motor vehicles, and motor vehicles at the intersection through the passing demand diagram, so that the passing demand intensities of the passing chain and phase at each moment of the whole day and for a period of time in the future can be accurately calculated and predicted.

[0015] 2) Based on the statistical analysis of historical data, the present invention dynamically predicts the time duration and probability required for traffic participants such as pedestrians, non-motor vehicles, and motor vehicles to reach the intersection. Compared with the current technical status of passively detecting traffic demands at adjacent intersections in the industry, it changes from passive to active, providing sufficient calculation time for subsequent intersection control.

[0016] 3) Based on the intersection scheduling strategy of demand coordination, the present invention can comprehensively consider the traffic demands of different types of traffic participants such as buses, 110 / 119 / 120 vehicles, private cars, and online car-hailing vehicles, and optimize the phase duration in combination with the dynamic changes in the total traffic demand to efficiently meet their traffic demands.

[0017] The present invention will be further described in detail below with reference to the accompanying drawings. Description of the Drawings

[0018] Figure 1 is a flowchart of the intersection right-of-way scheduling method for demand coordination of the present invention.

[0019] Figure 2 is a flowchart for calculating the traffic demand probability of traffic participants.

[0020] Figure 3 is a flowchart for constructing the time-series traffic demand diagram.

[0021] Figure 4 is a flowchart for switching the intersection right-of-way. Detailed Embodiments

[0022] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, such descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the fact that those skilled in the art can implement them. When the combination of technical solutions conflicts or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0024] In one embodiment, in combination with Figure 1 , a method for scheduling the intersection right-of-way for demand coordination is provided, including the following steps:

[0025] Step 1: Obtain static data information such as the spatial layout of the urban road network, roadside detection devices, and intersection distribution, as well as dynamic data information such as the real-time position data, OD data, and historical trajectory data of pedestrians, non-motor vehicles, and motor vehicles (motor vehicles, non-motor vehicles).

[0026] Step 2: Calculate and predict the time and probability of arriving at each intersection based on the OD data, historical trajectories, etc. of traffic travel subjects, combined with the spatial characteristics of the road network.

[0027] As Figure 2 shown, the calculation process of the arrival time and probability of the traffic demand is as follows:

[0028] Step 201: Obtain the position, driving direction, speed, and downstream intersection information of traffic travel subjects such as pedestrians, non-motor vehicles, and motor vehicles in the road network.

[0029] Step 202: Obtain historical travel time data, and use the clustering method to screen out the travel time data set T = [t 0 , t 1 , t 2 ,..., t n in the historical travel time data under similar traffic backgrounds. t i represents the travel time of a certain traffic travel subject i, where i = 1, 2,..., n, and n is the number of traffic travel subjects.

[0030] Step 203: Screen out historical travel time samples of the same type in the travel time data set to form a data set T' according to the individual characteristics of traffic travel subjects.

[0031] Step 204: Screen out the maximum travel time t max and the minimum travel time t min in the data set T'. Calculate the distribution probability of the historical travel time data in [0.9t min , 0.9t min +Δt], [0.9t min +Δt, 0.9t min +2Δt],..., [0.9t min +(n - 1)Δt, 0.9t min +nΔt], where nΔt≥t max , to obtain the probability set P = [P 0 , P 1 , P 2 ,..., P m . P j represents the distribution probability of the historical travel time data in the j-th interval, where j = 0, 1,..., m, and m + 1 is the total number of intervals.

[0032] Step 205, perform an accumulative calculation on the probability set P. If P 0 +P 1 +P 2 +...+P k ≥ p%, then use the corresponding t k and p% as the travel time and arrival probability of the traffic demand arriving at the intersection respectively, where k ≤ m;

[0033] Step 206, dynamically adjust the demand arrival time according to the change of the running position of the traffic travel subject until it arrives at the intersection.

[0034] Step 3, calculate the total traffic demand time series distribution within a future period of time at the intersection, and construct a time series traffic demand diagram, as Figure 3 shown below. The specific steps are as follows:

[0035] Step 301, design a data structure for the traffic demand at the intersection to store the traffic demands of different traffic travel subjects such as pedestrians, non-motor vehicles, and motor vehicles. The data structure fields include identity identification, import direction, export direction, traffic lane, demand level, demand probability, and arrival time. Among them, the identity identification is globally unique.

[0036] Step 302, according to the channelization and traffic rules of the intersection, exhaust all traffic chains, and superimpose the traffic demands of the traffic travel subjects with the traffic chains to form a time series traffic demand diagram of the intersection.

[0037] Here, from a certain import direction to an export direction at the intersection, a directed traffic chain is formed. The superposition of multiple traffic chains constitutes a time series traffic demand diagram of the intersection; the weight on the traffic chain represents the strength of the traffic demand.

[0038] Step 303, calculate the traffic demand values of the traffic chains, phases, and traffic travel subjects such as pedestrians, non-motor vehicles, and motor vehicles at the intersection.

[0039] The calculation formula for the traffic demand of the traffic travel subject is:

[0040] D i =α i *P i *w i *t i

[0041] where D i is the traffic demand of the traffic travel subject; α i is the probability adjustment coefficient, with a value range of [0.5, 1]. The closer to the intersection, the smaller the value of α i , and when stopped in front of the intersection, α i takes the value of 1; P iis the probability of arriving at the stop line of a certain approach lane at an intersection at a certain moment, w i is the level of traffic demand, t i is the duration from the moment of arriving at the intersection until the traffic demand response; i represents the i-th traffic travel entity;

[0042] The formula for calculating the traffic demand of traffic travel entities on the traffic link is:

[0043]

[0044] Among them, W j is the traffic demand on traffic link j, and n1 is the number of traffic travel entities on traffic link j;

[0045] The formula for calculating the traffic demand of traffic travel entities on a phase is:

[0046] S m =Max(W 1 , W 2 ,..., W n2 )

[0047] Among them, S m is the traffic demand of traffic travel entities on phase m, and n2 is the number of traffic links;

[0048] The formula for calculating the total traffic demand of an intersection is:

[0049]

[0050] Among them, C is the total traffic demand of the intersection, and n3 is the number of phases;

[0051] Step 304, correct the traffic demand on the traffic link, phase and intersection. According to the vehicle stop position, calculate the number of vehicles between the vehicle position and the stop line, so as to estimate the demand W L of the traffic link at this position, and compare it with the demand W 0 at the corresponding lane and position in the traffic demand diagram of the intersection. The error Δw = |W L - W 0 |. If ΔW exceeds the threshold, correct W 0 , and synchronously correct the traffic demand on the phase and intersection.

[0052] Step 4, schedule the right of way based on the intersection time-sequential traffic demand diagram. As Figure 4 shown, the specific process is as follows:

[0053] Step 401, taking the total traffic demand C of the intersection as the control target, divide the green light duration P t of the phase into two parts, the fixed green light duration Ps and the extended green light duration P e , P t = P s + P e ;

[0054] Step 402, calculate the fixed green light duration of the phase:

[0055] Initial calculation method: At the current moment T = 1, based on the phase passing demands of traffic travel entities that have stopped in front of the stop line and whose demands have been confirmed, calculate the fixed green light duration of the phase

[0056] Subsequent calculation method, at the current moment T = n, n > 1, the fixed green light duration Based on the phase passing demands of traffic travel entities that have stopped in front of the stop line and whose demands have been confirmed, calculate the required fixed green light duration Based on the extended green light duration when the previous phase is released, calculate β = [0, 8, 0.9, 1.0, 1.1, 1.2], confirmed according to the current time period, taking a higher value during peak hours and a lower value during other time periods;

[0057] Step 403, at the end moment of the green light period, calculate the predicted extension time Δt, with the value range [Δt min -Δt max ; Δt min 、Δt max Confirmed according to factors such as traffic travel entities, intersection spatial shape, conflict points, etc.; within the range of [Δt min -Δt max , calculate ΔC second by second and take its maximum value ΔC max , if ΔC max > 0, then take the Δt max corresponding to ΔC i as the extended phase time; if ΔC max ≤ 0, then this phase ends and the right of way transitions to the next phase;

[0058] Step 404, calculate the green light duration of the phase and release the next phase.

[0059] In one embodiment, a demand - coordinated intersection right - of - way scheduling system is provided. The system includes the following executed in sequence:

[0060] The first module is used to obtain the static data information and dynamic data information of urban roads; the static data information includes the road network spatial layout, roadside detection equipment, and intersection distribution, and the dynamic data information includes the real - time position data, OD data, and historical trajectory data of pedestrians, non - motor vehicles, and motor vehicles;

[0061] The second module is used to calculate and predict the time and probability of arriving at each intersection by combining the data obtained by the first module with the road network spatial characteristics;

[0062] The third module is used to calculate the total traffic demand time series distribution within a future period of time at the intersection and construct an intersection time series demand diagram;

[0063] The fourth module is used to schedule the right of way based on the intersection time series demand diagram.

[0064] For the specific limitations of the intersection right of way scheduling system regarding demand coordination, reference can be made to the limitations of the intersection right of way scheduling method for demand coordination in the above text, which will not be elaborated here. Each module in the above intersection right of way scheduling system for demand coordination can be implemented in whole or in part by software, hardware, and their combinations. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form to facilitate the processor to call and execute the operations corresponding to the above modules.

[0065] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0066] Step 1, obtaining the static data information and dynamic data information of urban roads; the static data information includes the road network spatial layout, roadside detection devices, and intersection distribution, and the dynamic data information includes the real-time position data of pedestrians, non-motor vehicles, and motor vehicles, OD data, and historical trajectory data;

[0067] Step 2, calculating and predicting the time and probability of arriving at each intersection by combining the data obtained in Step 1 with the road network spatial characteristics;

[0068] Step 3, calculating the total traffic demand time series distribution within a future period of time at the intersection and constructing an intersection time series demand diagram;

[0069] Step 4, scheduling the right of way based on the intersection time series demand diagram.

[0070] For the specific limitations of each step, reference can be made to the limitations of the intersection right of way scheduling method for demand coordination in the above text, which will not be elaborated here.

[0071] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0072] Step 1: Obtain the static data information and dynamic data information of urban roads; the static data information includes the road network spatial layout, roadside detection devices, and intersection distribution, and the dynamic data information includes the real-time position data of pedestrians, non-motor vehicles, and motor vehicles, OD data, and historical trajectory data;

[0073] Step 2: According to the data obtained in Step 1, combined with the spatial characteristics of the road network, calculate and predict the time and probability of arriving at each intersection;

[0074] Step 3: Calculate the total traffic demand time series distribution within a future period of time at the intersection, and construct an intersection time series demand diagram;

[0075] Step 4: Schedule the right of way based on the intersection time series demand diagram.

[0076] For the specific limitations of each step, reference can be made to the limitations of the intersection right-of-way scheduling method for demand coordination in the above text, which will not be elaborated here.

[0077] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for scheduling the right of way at intersections with demand collaboration, characterized in that, the method comprises the following steps: Step 1, obtain the static data information and dynamic data information of urban roads; the static data information includes the road network spatial layout, roadside detection equipment and intersection distribution, and the dynamic data information includes the real-time position data of pedestrians and vehicles, OD data and historical trajectory data; Step 2, according to the data obtained in Step 1, combined with the spatial characteristics of the road network, calculate and predict the time and probability of arriving at each intersection; Step 3, calculate the total time-series distribution of the traffic demand at the intersection in the future for a period of time, and construct an intersection time-series demand diagram; the specific process includes: Step 3-1, design the data structure of the traffic demand at the intersection to store the traffic demands of different traffic travel subjects; the data structure fields include identity identification, import direction, export direction, passing lane, demand level, demand probability, arrival time; among them, the identity identification is globally unique; Step 3-2, according to the channelization and passing rules of the intersection, exhaust all passing chains, and superimpose the traffic demands of traffic travel subjects on the passing chains to form an intersection time-series passing demand diagram; Step 3-3, calculate the traffic demands of traffic travel subjects on the passing chains, phases and intersections respectively; Step 3-4: Calibrate the passing chain, phase, and passing demands of traffic participants at the intersection. Based on the vehicle parking positions, obtain the number of vehicles between the vehicle position and the stop line to estimate the demand W of the passing chain at this position. L , and compare it with the passing demand W at the corresponding lane and position in the intersection time-sequential passing demand diagram. 0 Obtain the error ΔW = |W L - W 0 |. If ΔW exceeds the set threshold, then calibrate W 0 , and synchronously calibrate the phase and passing demands at the intersection. Step 4, schedule the right of way based on the intersection time-series demand diagram.

2. The method for scheduling the right of way at intersections with demand collaboration according to claim 1, characterized in that, the specific process of calculating and predicting the time and probability of arriving at each intersection according to the data obtained in Step 1 in Step 2, combined with the spatial characteristics of the road network, includes: Step 2-1, obtain the positions, driving directions, speeds and downstream intersection information of traffic travel subjects such as pedestrians and vehicles in the road network; Step 2-2, obtain historical travel time data, and use the clustering method to screen out the travel time data sets of pedestrians and vehicles under the same traffic background from the historical travel time data. The data set T = [t 0 , t 1 , t 2 , …, t n , where t i represents the travel time of a certain traffic travel subject i, i = 0, 1, 2, ..., n, and n + 1 is the number of traffic travel subjects; Step 2-3: According to the individual characteristics of the transportation subjects, screen out historical travel time samples of the same type from the travel time dataset to form dataset T ’ ; Step 2-4, in the data set T ’ filter out the maximum travel time t max and the minimum travel time t min . Taking Δt as the sliding window interval, calculate the distribution probability of the historical travel time data in [0.9t min , 0.9t min +Δt], (0.9t min +Δt, 0.9t min +2Δt],…, (0.9t min +(n-1)Δt, 0.9t min +nΔt], where nΔt≥t max . Obtain the probability set P = [P 0 , P 1 , P 2 ,…, P m , where P j represents the distribution probability of the historical travel time data in the j-th interval, j = 0, 1,..., m; m + 1 is the total number of intervals; Step 2-5, perform cumulative calculation on the probability set P. If P 0 +P 1 +P 2 +…+P k ≥ p%, then use the corresponding t k and p% as the travel time and arrival probability of the passing demand arriving at the intersection respectively, where k ≤ m; Step 2-6, dynamically adjust the demand arrival time according to the change of the vehicle running position until the vehicle arrives at the intersection.

3. The method for scheduling the right of way at intersections with demand collaboration according to claim 2, characterized in that, p% = 85% in Step 2-5.

4. The method for scheduling the right of way at intersections with demand collaboration according to claim 1, characterized in that, Step 3-3 specifically includes: The calculation formula for the traffic demand of traffic travel subjects is: D i = α i * P i * w i * t i Among them, D i is the traffic demand of the traffic entity; α i is the probability adjustment coefficient, with a value range of [0.5, 1]. The farther away from the intersection, the smaller the value of α i is. When stopping before the intersection, the value of α i is 1; P i is the probability of arriving at the stop line of a certain approach lane of the intersection at a certain moment. w i is the level of traffic demand, t i is the duration from the moment of arriving at the intersection until the traffic demand is responded to; i represents the i-th traffic entity; The calculation formula for the traffic demand of traffic travel subjects on the passing chain is: Among them, W j is the traffic demand on the passing chain j, and n1 is the number of traffic participants on the passing chain j; The calculation formula for the traffic demand of traffic travel subjects on the phase is: S m = Max(W 1 , W 2 , …, W n2 ) Among them, S m is the traffic demand of traffic subjects on phase m, and n2 is the number of traffic chains; The calculation formula for the total traffic demand at the intersection is: Among them, C is the total traffic demand at the intersection, and n3 is the number of phases.

5. The method for scheduling the right of way at intersections with demand collaboration according to claim 4, characterized in that, the specific process of scheduling the right of way based on the intersection time-series demand diagram in Step 4 includes: Step 4-1, taking the total intersection traffic demand C as the control target, divide the green light duration P of the phase t into two parts, the fixed green light duration P s and the extended green light duration P e , P t =P s +P e ; Step 4-2, calculate the fixed green light duration of the phase: Initial calculation method: At the current time T = 1, based on the phase passing demand of traffic travel entities that have stopped in front of the stop line and whose demands have been confirmed, calculate the fixed green light duration of the phase G γ is the start-up lost time, g i' is the green light time required for the passing chains included in the current phase, i' = 1, 2,..., n4, where n4 represents the number of passing chains; Subsequent calculation method: At the current moment T = n, where n > 1, the fixed green light duration β is a coefficient; based on the phase passing demand of traffic travel subjects that have stopped in front of the stop line and whose demands have been confirmed, calculate the required fixed green light duration Based on the extended green light duration during the release of the previous phase Calculate Step 4-3, at the end moment of the green light period, calculate the predicted extension time Δt, with the value range [Δt min , Δt max ; within the range of [Δt min , Δt max , according to the total intersection traffic demand calculation formula, calculate ΔC second by second, and take the maximum value ΔC max . If ΔC max >0, then take the extension time Δt max corresponding to ΔC i as the extended phase time; if ΔC max ≤0, then this phase ends and the right of way transitions to the next phase; Step 4-4, calculate the green light duration of the phase and release the next phase.

6. The method for scheduling the right of way at intersections with demand collaboration according to claim 5, characterized in that, β = [0.8, 0.9, 1.0, 1.1, 1.2] in Step 4-2, and is selected according to the current time period, and the value in the peak time period is higher than the value in other time periods.

7. An intersection right-of-way scheduling system for demand collaboration based on the method according to any one of claims 1 to 6, characterized in that, the system includes the following executed in sequence: A first module, configured to obtain static data information and dynamic data information of urban roads; the static data information includes road network spatial layout, roadside detection devices, and intersection distribution, and the dynamic data information includes real-time position data of pedestrians, non-motor vehicles, and vehicles, OD data, and historical trajectory data; A second module, configured to calculate and predict the time and probability of arriving at each intersection based on the data obtained by the first module and in combination with the spatial characteristics of the road network; A third module, configured to calculate the total traffic demand time series distribution within a future period of time at the intersection and construct an intersection time series demand diagram; A fourth module, configured to schedule the right-of-way based on the intersection time series demand diagram.

8. A computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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