A bus vehicle and social vehicle cooperative optimization green wave control method and system

Abstract

The application discloses a kind of bus vehicle and social vehicle coordination optimization green wave control method and system, belong to intelligent transportation technical field, including according to bus vehicle flow determination signal period;Obtain bus vehicle travel time, stop time is dynamically calculated according to the number of passengers, travel time is added into acceleration and deceleration delay;With bus vehicle from green light interval middle time through as target generation basic green wave scheme;Build lower layer bus dispatching model and upper layer signal timing model, launch interval and signal period satisfy integer multiple relationship, upper and lower layer iteration optimization;With social vehicle average travel time or "people x speed" index selects optimal scheme;System includes information acquisition unit, information processing unit, simulation decision unit and scheme implementation unit;The application can reduce the number of social vehicle parking and average travel time while ensuring that bus vehicle does not stop through intersection.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a green wave control method and system for coordinated optimization of public transport vehicles and private vehicles. Background Technology

[0002] With the continuous increase in the number of motor vehicles in cities, urban traffic congestion remains a persistent problem. Green wave control can effectively reduce vehicle delays at intersections, thereby improving road efficiency and alleviating urban traffic congestion. Due to the low passenger load factor of private cars and the development strategy of prioritizing public transportation, green wave control has gradually shifted from being designed primarily for private cars to prioritizing public transportation services.

[0003] Most existing bus signal priority methods lack a comprehensive consideration of traffic flow efficiency at road intersections. Common control strategies include extending green lights, ending red lights early, and phase skipping. However, these operations can only prioritize a very small number of buses, making it difficult to improve overall efficiency while ensuring a balance of interests among all parties. Furthermore, in existing bus priority green wave control methods, bus operating parameters (such as departure intervals and stop times) are usually fixed or empirical values, failing to consider the impact of dynamic changes in passenger flow at stops on stop times, and ignoring the actual impact of bus acceleration and deceleration on travel time, resulting in insufficient green wave timing accuracy. Simultaneously, bus scheduling and signal timing are usually optimized independently, lacking coordination and matching between bus departure intervals and signal cycles, making it difficult to ensure that every bus falls within the green wave zone. Existing technologies lack green wave control methods that simultaneously consider the traffic efficiency of both buses and other vehicles. In urban road traffic, the traffic interests of both buses and other vehicles are indispensable; how to ensure green wave passage for buses while also considering the efficiency of other vehicles is a pressing technical problem that needs to be solved in this field. Summary of the Invention

[0004] The purpose of this invention is to provide a green wave control method and system for coordinated optimization of public transport vehicles and private vehicles, so as to solve the technical problem that existing green wave control methods cannot simultaneously take into account the priority of public transport vehicles and the efficiency of private vehicle traffic, resulting in the inability to optimize the overall traffic efficiency of urban roads.

[0005] To achieve the above objectives, the present invention provides a green wave control method for coordinated optimization of public transport vehicles and private vehicles, comprising the following steps: S1: Take an urban arterial road with multiple intersections containing dedicated bus lanes as the design road, obtain the bus traffic flow on the design road, and determine the signal cycle of the intersection based on the bus traffic flow. S2: Obtain the travel time of a bus that stops only at bus stops on a bus lane without stopping at intersections. The travel time is determined based on the bus's stopping time at bus stops on the road segment and the bus's travel time on the road segment. The stopping time is dynamically calculated based on the number of passengers getting on and off at the bus stop and the time for each passenger to get on or off. The travel time also includes the acceleration and deceleration delay time when entering and leaving the station. S3: Taking two consecutive intersections and the road segment between the two intersections as an example, obtain the travel time of the bus in each road segment, obtain the signal cycle and green light time on the road segment, and determine the arrival time of the bus at each intersection based on the time when the bus arrives at the starting point of the previous road segment; with the goal of the bus passing through the middle time of the green light interval of each intersection, output the green light interval of each intersection respectively, and the green light interval of each intersection constitutes the basic green wave scheme for buses. S4: Obtain the travel time of multiple private vehicles on each road segment under conditions without traffic lights, and determine the average travel time of private vehicles on each road segment based on the travel time of multiple private vehicles under conditions without traffic lights. S5: Construct a lower-level bus dispatch model to optimize the departure interval, fleet size, and speed of buses with the goal of minimizing the average travel time cost per passenger. Based on the basic green wave scheme output in step S3, generate multiple adjustment schemes by changing the offset of the green light interval at the next intersection within the signal cycle, so that the arrival time of social vehicles at the next intersection is at the beginning of the green light interval, and the departure interval of buses satisfies an integer multiple relationship with the signal cycle. S6: Construct an upper-level signal timing model. Based on the bus speed, departure interval, and fleet size output by the lower-level model, optimize the signal cycle at intersections, the phase difference between adjacent intersections, and the speed of private vehicles with the goal of maximizing "pedestrian × vehicle speed". Feed the optimized signal cycle back to the lower-level model to re-optimize the departure interval. Iterate repeatedly until convergence. Combine the basic green wave scheme with various adjustment schemes to obtain multiple green wave control schemes. Select the optimal scheme by using the average travel time of private vehicles in the entire green wave segment or the "pedestrian × vehicle speed" index as the evaluation index.

[0006] Preferably, the signal cycle of the road intersection in S1 for: ; in, The reference signal cycle for a road intersection, in seconds; N It is an integer. N =1, 2, 3, 4, ...; Reference signal cycle at road intersections for: ; in, For bus traffic flow; N The specific method for determining the value is as follows: The optimal signal cycle length for each intersection along the road is determined using the Webster method. , take Greater than or equal to And closest of N As N The value of .

[0007] Preferably, buses in S2 are on the road section The travel time is as follows: ; in, For buses on the road section The travel time For buses on the road section Stop time at the bus stop. For buses on the road section Travel time; For the operating speed of public transport vehicles, , These are deceleration and acceleration, respectively.

[0008] Preferably, in S3, two consecutive intersections and For example, the road segments corresponding to the two intersections are respectively and Buses on the road and road sections The travel times are respectively and The signal period is The green light time is Public buses exist Arrival time section Starting point; intersection The green light interval is: [ , ]; intersection The green light interval is: [ , ]; in, For public transport vehicles Arrival Section The starting point time, It is a non-negative integer.

[0009] Preferably, in S4, social vehicles on the road section The average travel time on the platform is: ; in, Indicates road segment The average travel time for normal passage of social vehicles. Indicates road segment Upper The normal travel time for a private vehicle. Indicates road segment The total number of social vehicles that are normally passing through.

[0010] Preferably, in S6, the PTV-VISSIM simulation software is used for simulation. By comparing the average travel time of social vehicles in the entire green wave under each scheme, the scheme with the minimum average travel time is taken as the optimal scheme.

[0011] Preferably, the stop time in S2 is dynamically calculated according to the following formula: ; in, , These represent the number of people boarding and alighting at the platform, respectively. , These are the times required for a single passenger to board and alight, respectively. S5 departure interval With signal period satisfy: ; in, It is a positive integer.

[0012] Preferably, the objective function of the lower-level model is: ; in, , , These represent the total cost of waiting time for public transport passengers, the total cost of riding the bus, and the total cost of operating time for the intelligent bus fleet, respectively. For the first Passenger flow at each station; The objective function of the upper-level model is: ; in, For the first Station to the Total passenger flow between stations , These represent the passenger flow sharing ratios for public transportation and private vehicles, respectively. These represent the speeds of public buses and private vehicles, respectively.

[0013] Preferably, if buses operate in platoons, then the minimum value of the green wave band width in the upper-level model is... Determined based on the fleet size output by the lower-level model: ; in, This represents the number of vehicles in the convoy. This refers to the headway of the train. During the iteration of the upper and lower layer models, the passenger flow sharing ratio is updated using the Logit model based on the improvement of public transportation operation efficiency: ; ; in, , The travel utility of choosing public transportation and private vehicles is considered respectively. Based on this, the bus origin-destination (OD) demand of each station is updated and the next layer of optimization is carried out.

[0014] A green wave control system for coordinated optimization between public transport vehicles and private vehicles includes: The information collection unit is used to obtain the actual pedestrian flow characteristics of bus stops on the road segment and the traffic flow characteristics of social vehicles on the road segment; The information processing unit is used to determine the frequency of bus departures and the stopping time of buses at bus stops based on the actual passenger flow characteristics of bus stops obtained by the information collection unit; to determine the average travel time of social vehicles on the road segment based on the traffic flow characteristics of social vehicles obtained by the information collection unit; and to determine the intersection signal cycle based on the frequency of bus departures, and to determine the travel time of buses on the road segment based on the stopping time of buses at bus stops and the travel time of buses on the road segment. The simulation decision-making unit is used to determine the green light intervals at each intersection based on the signal cycle determined by the information processing unit, the travel time of buses on each road segment, and the average travel time of private vehicles on each road segment, thus obtaining a basic green wave scheme for buses. Based on the basic green wave scheme, it generates multiple adjustment schemes by changing the offset of the green light interval at the next intersection within the signal cycle. The basic green wave scheme is combined with these adjustment schemes to obtain various green wave control schemes. Finally, the simulation module is invoked to evaluate these green wave control schemes, using the average travel time of private vehicles throughout the green wave segment or the "pedestrian x vehicle speed" index as the evaluation metric to select the optimal scheme from the multiple green wave control schemes. The implementation unit is used to connect to the traffic signal control system and run the optimal solution selected by the simulation decision unit.

[0015] Therefore, the green wave control method and system for coordinated optimization of public transport vehicles and private vehicles described above have the following beneficial effects: (1) The present invention determines the signal cycle based on the departure frequency of buses and designs a basic green wave scheme with the goal of buses passing through the middle of the green light interval of each intersection. Under the condition of bus lane, it can ensure that buses only stop at bus stops and do not stop at intersections, so as to realize the full green wave passage of buses at continuous intersections.

[0016] (2) The present invention dynamically calculates the stopping time based on the number of passengers getting on and off at the bus stop and includes the acceleration and deceleration delays when entering and leaving the station in the travel time, so that the green wave scheme design is more in line with the actual operating characteristics of buses and improves the accuracy and reliability of green wave timing.

[0017] (3) By constraining the departure interval of buses to meet the integer multiple relationship with the signal cycle, this invention ensures that every bus departure can fall into the green wave, avoids some vehicles missing the green wave due to phase mismatch, and ensures the stability and consistency of the bus priority effect.

[0018] (4) This invention constructs a two-layer model for the coordinated optimization of bus scheduling and signal timing. The lower layer optimizes the departure interval, fleet size and driving speed with the goal of minimizing the average travel time cost per passenger. The upper layer optimizes the signal timing with the goal of maximizing "person × vehicle speed". Through iterative feedback between the upper and lower layers, the matching of bus scheduling and signal green wave is realized, which effectively improves the operating efficiency of bus vehicles and the traffic efficiency of the entire transportation network.

[0019] (5) Based on ensuring the green wave passage of public buses, this invention generates multiple adjustment schemes by changing the offset of the green light interval of the next intersection within the signal cycle, and selects the scheme that makes the arrival time of social vehicles closest to the start of the green light. This can effectively reduce the number of stops and average travel time of social vehicles at intersections, and improve the travel experience of social vehicle passengers.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] Figure 1 This is a flowchart of a green wave control method for coordinated optimization of public transport vehicles and private vehicles according to the present invention. Figure 2 This is a schematic diagram of an example road segment from an embodiment of the present invention; Figure 3 This is a schematic diagram of a green wave control scheme for public buses, as exemplified by an embodiment of the present invention. Figure 4A schematic diagram illustrating the optimal green wave control scheme considering both public transport vehicles and private vehicles, as exemplified in an embodiment of the present invention. Figure 5 This is a framework diagram of the upper and lower layer collaborative optimization model in an embodiment of the present invention; Figure 6 This is a flowchart illustrating the iterative solution process of the upper and lower layer collaborative optimization model in an embodiment of the present invention. Figure 7 This is a diagram of the architecture of a green wave control system for collaborative optimization between public transport vehicles and private vehicles according to the present invention. Figure 8 This is a structural diagram of a green wave control system for coordinated optimization of public transport vehicles and private vehicles according to the present invention. Reference numerals: 500, server; 501, information receiver; 502, information storage; 503, central processing unit; 504, communication interface; 601, information acquisition unit; 602, information processing unit; 603, simulation decision-making unit; 604, scheme implementation unit. Detailed Implementation

[0022] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely illustrates selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] Example like Figure 1 As shown, this invention provides a green wave control method for coordinated optimization of public transport vehicles and private vehicles, comprising the following steps: S1: Select urban arterial roads with dedicated bus lanes and continuous intersections as the design road segment, obtain the bus traffic flow on the design road segment, and determine the signal cycle of the intersections based on the bus traffic flow. The selection of the design road segment should meet the following conditions: the departure frequency of buses operating on the road segment is fixed within a unit of time, that is, the departure time is uniform; the operating speed of buses is stable.

[0024] Signal cycle at road intersections for: ; in, The reference signal cycle for a road intersection, in seconds; N It is an integer. N =1, 2, 3, 4, ...

[0025] Reference signal cycle at road intersections for: ; in, This represents the flow of public transport vehicles, expressed in vehicles per hour. N The specific method for determining the value is as follows: The optimal signal cycle length for each intersection along the road is determined using the Webster method. , take Greater than or equal to And closest of N As N The value of is determined by the Webster's optimal signal period calculation formula. ,in The total signal loss time. This is the periodic flow rate ratio.

[0026] Determining the signal cycle based on the frequency of bus departures allows the signal cycle to match the rhythm of bus operations, laying the foundation for buses to "stop at intersections without stopping at stations" in the future.

[0027] S2: Obtain the travel time of buses that only stop at bus stops on bus lanes without stopping at intersections. The travel time is determined based on the bus's stop time at bus stops along the road segment and the bus's travel time along the road segment. The stop time is dynamically calculated based on the number of passengers getting on and off at the bus stop and the time each passenger takes to get on and off, to reflect the impact of fluctuations in passenger demand on the stop time and avoid the green wave timing deviation caused by using a fixed stop time.

[0028] Buses on the road The travel time is as follows: ; in, For buses on the road section The travel time is in seconds. For buses on the road section The stopping time at the bus stop, in seconds. For buses on the road section The travel time, in seconds; The speed of the bus is expressed in meters per second. , These are deceleration and acceleration, respectively, in meters per second. 2 .

[0029] Including acceleration and deceleration delays at stations allows for more accurate travel time calculations that better reflect the actual operating characteristics of buses, improving the precision and reliability of green wave timing.

[0030] Stop time is dynamically calculated using the following formula: ; in, , These represent the number of people boarding and alighting at the platform, respectively. , These are the times required for a single passenger to board and alight, respectively; the stop time is calculated in real time based on the number of passengers boarding and alighting, which can adapt to changes in passenger flow demand at different times and avoid deviations in the green wave scheme caused by the assumption of fixed stop times.

[0031] S3: Taking two consecutive intersections and the corresponding road segments as an example, obtain the travel time of buses on each road segment, obtain the signal cycle and green light time on the road segment, and determine the arrival time of buses at each intersection based on the time when buses arrive at the starting point of the previous road segment; with the goal of buses passing through the middle time of the green light interval of each intersection, output the green light interval of each intersection respectively, and the green light interval of each intersection constitutes the basic green wave scheme for buses.

[0032] If buses operate in platoons, the minimum green wave bandwidth of the basic green wave scheme is determined based on the platoon size and headway. Minimum Green Wave Bandwidth for Platoon Buses Calculate using the following formula: ; in, This represents the number of vehicles in the convoy. This is the headway; to ensure that the entire convoy can pass through the intersection continuously, and to prevent some vehicles from being stopped by the red light due to insufficient green wave width.

[0033] Specifically, such as Figure 2 As shown, taking a city road with three lanes in one direction and six lanes in two directions as an example, the two sides are side roads, the rightmost lane is a dedicated bus lane, and there is a bus stop in the middle of each section. Taking two consecutive intersections... and For example, the road segments corresponding to the two intersections are respectively and Buses on the road and road sections The travel times are respectively and The signal period is The green light time is The yellow light time is Red light time is Public buses exist Arrival time section The starting point, then its location Arrive at the intersection at the right time ,exist Arrive at the intersection at the right time .

[0034] If the traffic light is Start running at any time, intersection The green light interval is: [ , ]; Buses It passes through the middle of the green light interval, thus avoiding the intersection.

[0035] Similarly, intersections The green light interval is: [ , ]; Buses Similarly, it passes through the middle of the green light interval to avoid the intersection.

[0036] in, For public transport vehicles Arrival Section The starting point time, It is a non-negative integer.

[0037] By locking the arrival time of buses at the intersection in the middle of the green light interval, the reliability margin of bus passage can be maximized. Even if there are small fluctuations in operation, it can still be guaranteed that buses can pass through the intersection without stopping.

[0038] S4: Obtain the normal travel time of multiple private vehicles on each road segment, and determine the average travel time of private vehicles on each road segment based on the normal travel time of multiple private vehicles.

[0039] Social vehicles on the road section The average travel time on the platform is: ; in, Indicates road segment The average travel time for vehicles to pass through the area normally, in seconds. Indicates road segment Upper The normal travel time for a vehicle to pass through other vehicles, in seconds. Indicates road segment The total number of social vehicles that are normally passing through.

[0040] Obtaining the average travel time of private vehicles under conditions without traffic light obstruction is to distinguish the actual improvement effect of green wave design on private vehicles in subsequent adjustments to the green wave scheme, and to avoid confusion caused by traffic light delays.

[0041] S5: Construct a lower-level public transport dispatch model to optimize the departure interval, fleet size, and driving speed of public transport vehicles, with the goal of minimizing the average travel time cost per passenger.

[0042] The objective function of the lower-level model is: ; in, , , These represent the total cost of waiting time for public transport passengers, the total cost of riding the bus, and the total cost of operating time for the intelligent bus fleet, respectively. For the first Passenger flow at each station.

[0043] The lower-level model optimizes departure intervals, fleet size, and travel speed, enabling bus dispatching schemes to adapt to changes in passenger flow demand at stations, thereby reducing bus operating costs while ensuring passenger comfort.

[0044] Total cost of waiting time Based on passenger arrival patterns at the station: Assuming passengers arrive at a uniform rate, the number of passengers who can board the first bus is calculated. ,in For the bus fleet to arrive at the The number of seats remaining on the train at each stop. For the first Passenger flow at each station; number of passengers who missed their stop and had to wait for a second time. Total cost of travel time Calculate the total cost of fleet operation time based on the OD matrix. This includes vehicle travel time and fixed operating costs. The model is a linear optimization problem, solved directly using Python's gurobipy library. The main constraints involved in the model include: upper and lower limits on bus speed; a recursive constraint on the number of remaining seats when the fleet arrives at each stop; a constraint that the number of passengers boarding at each stop does not exceed the number of remaining seats; and a constraint on stop time calculated based on the number of passengers boarding and alighting. During the solution process, the optimization algorithm automatically satisfies these constraints within the feasible region.

[0045] Based on the basic green wave scheme output in step S3, taking two consecutive intersections and their corresponding road segments as an example, the following data is obtained: the time when buses pass through the previous intersection, the time when the red light at the previous intersection turns green, the travel time of buses on the subsequent road segment, and the average travel time of other vehicles on the subsequent road segment. Based on these times and travel times, the arrival times of buses and other vehicles at the next intersection are determined. Assuming the arrival time of buses at the next intersection falls within the green light interval, the green light timing at the next intersection is adjusted by changing the offset of the green light interval within the signal cycle, ensuring that the arrival time of other vehicles falls at the beginning of the green light interval. The departure interval of buses is an integer multiple of the signal cycle to ensure that every departing bus falls within the green wave band, preventing some vehicles from missing the green wave due to phase mismatch. Different offsets correspond to various adjustment schemes.

[0046] Specifically, with two consecutive intersections and For example, the road segments corresponding to the two intersections are respectively and The signal period is Buses on the road section The journey time is Social vehicles on the road section The average travel time on the platform is Intersection The green light time interval is [ , ],in It is a non-negative integer. and These are the relative baseline values ​​for the start and end times of the green light interval, respectively.

[0047] Buses Through the intersection The time of the traffic light is recorded as intersection The moment when the traffic light changes from red to green is denoted as ,in For public transport vehicles Through the intersection The time difference between the start time of the green light at that intersection and the start time of the green light. (Bus vehicles) Arriving at the intersection The time is The first private vehicle arrived at the intersection The time is .

[0048] To ensure that, while maintaining a green wave for public buses, private vehicles can obtain as many green wave opportunities as possible, the adjustment will meet the following requirements: Public buses Arrival Time Located in the green light zone , Within, and the arrival time of the first private vehicle. Located within the green light zone and close to Time. Departure interval With signal period satisfy: ; in, It is a positive integer.

[0049] By constraining the departure interval to an integer multiple of the signal cycle, it can be ensured that each departing bus arrives at the intersection in the same phase as the preceding bus, thus ensuring that all buses can enjoy the green wave effect and avoiding the problem in traditional methods where some buses cannot enjoy the green wave due to random departure phases.

[0050] S6: Construct an upper-level signal timing model. Based on the bus speed, departure interval, and fleet size output by the lower-level model, optimize the signal cycle at intersections, the phase difference between adjacent intersections, and the speed of other vehicles with the goal of maximizing "person × vehicle speed". Feed the optimized signal cycle back to the lower-level model to re-optimize the departure interval. Iterate repeatedly until convergence.

[0051] The objective function of the upper-level model is: ; in, For the first Station to the Total passenger flow between stations , These represent the passenger flow sharing ratios for public transportation and private vehicles, respectively. These represent the speeds of public buses and private vehicles, respectively.

[0052] Explanation of the function of the upper-level model: With the goal of maximizing "person × vehicle speed", it can simultaneously take into account the characteristics of large passenger capacity of public buses and the demand for traffic efficiency of social vehicles. It optimizes signal timing from the perspective of the entire traffic network and avoids the overall benefit loss caused by traditional methods that only focus on traffic flow or the passage time of a single type of vehicle.

[0053] The "person x vehicle speed" index refers to the product of the number of people passing through per unit of time (i.e., hour) and their average speed. This index comprehensively considers travel efficiency (from the perspective of travelers) and facility utilization efficiency (from the perspective of managers). When buses have a higher share of passenger flow, it can more accurately reflect the overall traffic efficiency improvement brought about by bus priority.

[0054] In the upper-level model, the constraints on the green wave band width are established based on the MAXBAND model. The travel time of buses between adjacent intersections... It consists of travel time, stop time at stations, and acceleration / deceleration delay time; travel time for private vehicles. The timing of the green wave is determined based on the road segment length and the speed of other vehicles. The bus green wave and the other vehicle green wave share the same signal timing scheme, and the phase difference between them must be an integer multiple. This model is also solved using the gurobipy library. The constraints involved in the model mainly include: the bus green wave width not exceeding the corresponding intersection green light duration; the other vehicle green wave width not exceeding the corresponding intersection green light duration; the phase difference between bus green waves at adjacent intersections being an integer multiple of the signal cycle; the phase difference between other vehicle green waves at adjacent intersections being an integer multiple of the signal cycle; the phase difference between bus green waves and other vehicle green waves being an integer multiple of the signal cycle; upper and lower limits for other vehicle speeds; upper and lower limits for the signal cycle; and non-negativity constraints for all green wave width and phase parameters. During the solution process, the optimization algorithm automatically satisfies the above constraints within the feasible region.

[0055] like Figure 6 As shown, the specific iteration process of the upper and lower layer models is as follows: (1) Set the speed of the bus The initial value is ,implement ,make from Traverse downwards and satisfy... ; Bus operating speed meets ,in Minimum permissible driving speed, The maximum permissible driving speed; (2) Solve for the bus fleet size when the "average travel time cost per passenger" is minimized in the lower-level model. and departure interval ; (3) The size of the convoy Passed to the upper-level model; (4) Solve for the signal period when “human × speed” is at its maximum in the upper-level model. Phase difference and the speed of social vehicles , will the signal period Pass the data to the lower-level model; (5) Add constraints to the lower-level model A new solution is obtained by solving it again. and ; (6) If If it converges, then it will converge; otherwise it will converge. Return the process to the upper-level model and iterate repeatedly. (7) Reduce the bus speed by 1 and repeat the above process until all speed values ​​are traversed; (8) Output the scheme that maximizes “human × speed” and all its parameters.

[0056] Through iterative iterations between upper and lower level models, bus scheduling schemes and signal timing schemes can be matched and optimized in a coordinated manner, achieving the effect of buses "stopping at stations but not at intersections," while improving the overall traffic efficiency of the transportation network.

[0057] During the iterative process of the upper and lower layer models, based on the improvement of public transportation operation efficiency, the Logit model is used to predict passenger flow shifting from private vehicles to public transportation. This determines the utility of travelers choosing public transportation. The utility of choosing social vehicles ,in, For travel duration, For travel expenses, , These are the parameters to be calibrated in the public transport utility function: travel time and travel cost. , These are the parameters to be calibrated in the social vehicle travel utility function, namely travel time and travel cost. This is a constant term in the utility function of social vehicle travel, which is a parameter to be calibrated. Public transport passenger share ratio. Share of social vehicles They are respectively: ; ; By predicting the passenger flow that will shift from private vehicles to public transport after the improvement of public transport efficiency, the demand for public transport origin-destination (OD) at each station can be dynamically updated, enabling the green wave scheme to adapt to the changed traffic composition and avoid insufficient public transport capacity or mismatch between signal timing and demand caused by passenger flow shift.

[0058] The basic green wave scheme output in step S3 is combined with the various adjustment schemes generated in step S5 to obtain multiple green wave control schemes. The multiple green wave control schemes are evaluated by simulation. The average travel time of social vehicles in the entire green wave band is used as the evaluation index, or the "person × vehicle speed" index is used as the evaluation index. The optimal scheme is selected from the multiple green wave control schemes.

[0059] Before applying them to real-world scenarios, simulations can be used to evaluate the effectiveness of different green wave control schemes. In this embodiment, the VISSIM simulation software (PTV-VISSIM) developed by PTV GmbH in Germany is used for simulation. By comparing the average travel time of vehicles in the entire green wave band under each scheme, the scheme with the minimum average travel time is selected as the optimal scheme.

[0060] The above method will be further illustrated with specific examples below.

[0061] like Figure 2 As shown, this embodiment selects three consecutive intersections of urban roads with three lanes in one direction and six lanes in two directions, with side roads on both sides and the rightmost lane being a dedicated bus lane. Each road segment has a bus stop in the middle. The entire green wave controlled section is 1250m long. The simulation only uses the right-hand road. The expected speed distribution for buses is 40km / h, and the expected speed distribution for other vehicles is 60km / h. The bus traffic flow is set at 60veh / h, and the other vehicle traffic flow is set at 1600veh / h. The simulation dynamically calculates the stopping time based on the number of passengers boarding and alighting at the bus stop and the time each passenger takes to board or alight. In this embodiment, the average stopping time is approximately 10s. The total simulation length is set to 6000s, with the first 1800s being a warm-up period. Data collected from the 1800s to 5400s simulation segment is used as the simulation result.

[0062] Based on a bus traffic flow of 60veh / h, the signal period is calculated in step S1 as follows: s; The green light duration is set to 30 seconds, the yellow light duration to 3 seconds, and the red light duration to 27 seconds.

[0063] Simulations show that the travel time for buses on each road segment is consistently around 55 seconds. Based on the signal cycle and the travel time of buses on each segment, the most conservative green wave control scheme is designed according to step S3, where buses pass through the intersection at the midpoint of the green light period. Figure 3 As shown, the blue line represents the bus's trajectory, and the green stripe represents the green wave zone for buses. As long as buses operate within this area, they can pass through intersections smoothly without stopping. This basic green wave signal control scheme is shown in Table 1, and the control scheme is implemented immediately upon the start of the simulation.

[0064] Table 1. Green wave signal control scheme for buses passing through the middle of a green light interval.

[0065] In step S4, under conditions without traffic light obstruction, the simulation shows that the travel time of social vehicles on each road segment is stable at 25s to 26s.

[0066] Based on the green wave signal control scheme S1 in Table 1, and combined with the travel time of social vehicles under traffic light-free conditions, five different green wave signal control schemes S2 to S6 are obtained by adjusting according to step S5. The six green wave control schemes are shown in Table 2. It should be noted that the green light duration and red light duration for each intersection listed in Tables 1 and 2 refer to the cumulative duration of the intersection actually being in green or red light mode within the time window from the simulation start time 0 to the end of the first signal cycle (60s). Due to the phase difference between adjacent intersections, some intersections are not at the start time of the green or red light at time 0. Therefore, the green or red light in the table may be divided into two segments by the cycle boundary, and their total value equals the complete green or red light duration within one complete signal cycle of the intersection. Appendix Figure 3 Appendix Figure 4 The green light duration and red light duration marked in the red box are the complete green light or red light duration within one complete signal cycle at the intersection.

[0067] Table 2 Six optional green wave control schemes

[0068] In step S6, PTV-VISSIM is used to simulate the operating conditions under six scenarios to obtain the travel time of private vehicles and public buses. The simulation results are shown in Table 3.

[0069] Table 3. Operation status of green wave road sections under six green wave control methods

[0070] Table 3 shows that under the six green wave signal control schemes, the travel time of buses on green wave sections is very stable, hovering around 170 seconds. The average travel time of other vehicles on green wave sections varies considerably, with the longest average travel time being 137.59 seconds (Scheme S1) and the shortest being 94.74 seconds (Scheme S6). The shortest average travel time is 31.14% lower than the longest. Therefore, green wave signal control scheme S6 is the best among the six options. Figure 4 As shown, the specific control method of the green wave signal control scheme S6 is the optimal green wave control scheme of the present invention.

[0071] like Figure 5 and Figure 6 As shown, the core of this embodiment lies in achieving mutual matching between bus scheduling and signal green waves through a collaborative optimization model between upper and lower layers. Figure 5This is a framework diagram of the upper and lower layer collaborative optimization model in this embodiment. It shows the overall architecture of the upper layer model optimizing signal timing with the goal of maximizing "person × vehicle speed", the lower layer model optimizing bus scheduling with the goal of minimizing the average travel time cost per passenger, and the upper and lower layers iterating with each other through key variables. Figure 6 This is a flowchart illustrating the iterative solution process of the upper and lower layer collaborative optimization model in this embodiment. It details the complete solution process from initial velocity setting, lower-layer scheduling optimization, upper-layer timing optimization, integer multiple constraint feedback, to iterative convergence. The simulation results of this embodiment verify the effectiveness of the above-described upper and lower layer collaborative optimization model.

[0072] like Figure 7 and Figure 8 As shown, a green wave control system for coordinated optimization between public transport vehicles and private vehicles includes an information acquisition unit, an information processing unit, a simulation decision-making unit, and a scheme implementation unit. In terms of hardware architecture, as... Figure 7 As shown, the system can be implemented based on server 500, which includes an information receiver 501, an information storage unit 502, a central processing unit 503, and a communication interface 504. Server 500 can be located in a traffic control center, or it can be a server in another branch center or an independent server. The central processing unit 503 contains the processing modules for calculation, simulation, and judgment of the entire server 500, serving as the control center. The central processing unit 503 can extract and calculate information obtained through the information receiver 501, store the processed results in the information storage unit 502, propose possible green wave signal control schemes through calculation, and call the simulation module to obtain traffic operation conditions under different schemes, ultimately determining the optimal control scheme. Furthermore, the central processing unit 503 can access the traffic control system through the communication interface 504 to apply the scheme to the actual traffic environment.

[0073] The information receiver 501 can use external monitoring equipment such as cameras to collect and receive real-time passenger flow information from bus stops, or it can use video surveillance or loop detectors to collect and receive traffic data from vehicles on the road segment. The information storage unit 502 mainly includes a program storage area and a data storage area. The program storage area can be used to store software programs, operating systems, and simulation modules, while the data storage area can be used to store data acquired by the information receiver 501, calculated data, simulated data, and other data. The communication interface 504 can be used to communicate with terminal devices, sending and receiving transmission information to the terminal devices, or to communicate with the traffic signal control system to achieve signal transmission.

[0074] In terms of functional logic, such as Figure 8 As shown, the system includes an information acquisition unit 601, an information processing unit 602, a simulation decision-making unit 603, and a scheme implementation unit 604.

[0075] It should be noted that, Figure 7 and Figure 8 The system of the present invention is described from different perspectives: Figure 7 This section describes the physical deployment of the system at the hardware architecture layer. Figure 8 The functional module layer illustrates the logical functional division of the system. Figure 7 yes Figure 8 The hardware carrier and physical implementation basis, Figure 8 Each functional module is based on Figure 7 The hardware resources shown are in operation. Specifically, the information receiver 501 is the hardware interface for the information acquisition unit 601 to perform data acquisition tasks; the information memory 502 provides unified data storage resources for each functional unit; the central processing unit 503 provides a computing and processing environment for the information processing unit 602, the simulation decision-making unit 603, and the scheme implementation unit 604; and the communication interface 504 provides a physical channel for communication between the scheme implementation unit 604 and the external traffic signal control system. The data flow between the units is as follows: the data acquired by the information acquisition unit 601 is transmitted to the information processing unit 602; the data processed by the information processing unit 602 is transmitted to the simulation decision-making unit 603; and the optimal scheme generated by the simulation decision-making unit 603 is output to the traffic signal control system via the scheme implementation unit 604.

[0076] The information collection unit 601 is used to acquire the actual pedestrian flow characteristics at bus stops along the road segment and the traffic flow characteristics of private vehicles along the road segment. The actual pedestrian flow characteristics acquired by the information collection unit 601 include the number of people waiting for buses at different times and the time required to board and alight the bus at different pedestrian volumes. The traffic flow characteristics acquired by the information collection unit 601 include the traffic volume and speed characteristics of the road segment at different times.

[0077] The information processing unit 602 is used to determine the frequency of bus departures and the dwell time of buses at bus stops based on the actual pedestrian flow characteristics acquired by the information collection unit 601. Specifically, the information processing unit 602 determines the frequency of bus departures and the dwell time of buses at bus stops for different time periods based on the number of people waiting for buses at different times and the time required to get on and off buses at different pedestrian volumes. The information processing unit 602 also determines the average travel time of social vehicles on the road segment for different time periods based on the traffic flow characteristics of social vehicles acquired by the information collection unit 601. The information processing unit 602 also determines the intersection signal cycle based on the frequency of bus departures, and determines the travel time of buses on the road segment based on the dwell time of buses at bus stops, the travel time of buses on the road segment, and the acceleration and deceleration delay time. The information processing unit 602 is also used to ensure that the departure interval and the signal cycle are integer multiples of each other, and is used to calculate the "person × vehicle speed" index.

[0078] The simulation decision unit 603 determines the green light intervals at each intersection based on the signal cycle determined by the information processing unit 602, the travel time of buses on each road segment, and the average travel time of private vehicles on each road segment, thus obtaining a basic green wave scheme for buses. The simulation decision unit 603 also generates multiple adjustment schemes by changing the offset of the green light interval at the next intersection within the signal cycle, based on the basic green wave scheme. The simulation decision unit 603 combines the basic green wave scheme with these multiple adjustment schemes to obtain various green wave control schemes, and then calls the simulation module to evaluate these schemes. The optimal scheme is selected from the multiple green wave control schemes using the average travel time of private vehicles in the entire green wave segment or the "pedestrian x vehicle speed" index as the evaluation metric. Specifically, the simulation decision unit 603 calls the PTV-VISSIM simulation module to perform simulation analysis, compares the average travel time of private vehicles in the entire green wave segment under each scheme based on the simulation results, and obtains the optimal scheme. If buses operate in platoons, the simulation decision unit 603 sets the minimum value of the green wave band width based on the platoon size and headway when generating the basic green wave scheme.

[0079] The scheme implementation unit 604 is used to connect to the traffic signal control system and run the optimal scheme selected by the simulation decision unit 603. Specifically, the scheme implementation unit 604 matches the optimal green wave control scheme to the traffic signal control system according to the program requirements of the traffic signal control system, and finally it is put into operation.

[0080] Therefore, the present invention adopts the above-mentioned green wave control method and system for coordinated optimization of public transport vehicles and private vehicles. While ensuring that public transport vehicles can pass through intersections without stopping, it can reduce the number of times private vehicles stop, shorten the average travel time of private vehicles in green wave sections, improve the overall traffic efficiency of urban roads, and improve the travel experience of private vehicle passengers.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A green wave control method for coordinated optimization of public transport vehicles and private vehicles, characterized in that, Includes the following steps: S1: Take an urban arterial road with multiple intersections containing dedicated bus lanes as the design road, obtain the bus traffic flow on the design road, and determine the signal cycle of the intersection based on the bus traffic flow. S2: Obtain the travel time of a bus that stops only at bus stops on a bus lane without stopping at intersections. The travel time is determined based on the bus's stopping time at bus stops on the road segment and the bus's travel time on the road segment. The stopping time is dynamically calculated based on the number of passengers getting on and off at the bus stop and the time for each passenger to get on or off. The travel time also includes the acceleration and deceleration delay time when entering and leaving the station. S3: For two consecutive intersections and the road segment between the two intersections, obtain the travel time of buses on each road segment, obtain the signal cycle and green light time on the road segment, and determine the arrival time of buses at each intersection based on the time when buses arrive at the starting point of the previous road segment; with the goal of buses passing through the middle time of the green light interval of each intersection, output the green light interval of each intersection respectively, and the green light interval of each intersection constitutes the basic green wave scheme for buses. S4: Obtain the travel time of multiple private vehicles on each road segment under conditions without traffic lights, and determine the average travel time of private vehicles on each road segment based on the travel time of multiple private vehicles under conditions without traffic lights. S5: Construct a lower-level bus dispatch model to optimize the departure interval, fleet size, and speed of buses with the goal of minimizing the average travel time cost per passenger. Based on the basic green wave scheme output in step S3, generate multiple adjustment schemes by changing the offset of the green light interval at the next intersection within the signal cycle, so that the arrival time of social vehicles at the next intersection is at the beginning of the green light interval, and the departure interval of buses satisfies an integer multiple relationship with the signal cycle. S6: Construct an upper-level signal timing model. Based on the bus speed, departure interval, and fleet size output by the lower-level model, optimize the signal cycle at intersections, the phase difference between adjacent intersections, and the speed of private vehicles with the goal of maximizing "pedestrian × vehicle speed". Feed back the optimized signal cycle to the lower-level model to re-optimize the departure interval. Iterate repeatedly until convergence. Combine the basic green wave scheme with various adjustment schemes to obtain multiple green wave control schemes. Select the optimal scheme by using the average travel time of private vehicles in the entire green wave segment or the "pedestrian × vehicle speed" index as the evaluation index.

2. The green wave control method for coordinated optimization of public transport vehicles and private vehicles according to claim 1, characterized in that, Signal cycle at road intersections in S1 for: ； in, The reference signal cycle for a road intersection, in seconds; N It is an integer. N =1, 2, 3, 4, ...; Reference signal cycle at road intersections for: ； in, For bus traffic flow; N The specific method for determining the value is as follows: The optimal signal cycle length for each intersection along the road is determined using the Webster method. , take Greater than or equal to And closest of N As N The value of .

3. The green wave control method for coordinated optimization of public transport vehicles and private vehicles according to claim 1, characterized in that, Buses in S2 section The travel time is as follows: ； in, For buses on the road section The travel time For buses on the road section Stop time at the bus stop. For buses on the road section Travel time; For the operating speed of public transport vehicles, , These are deceleration and acceleration, respectively.

4. The green wave control method for coordinated optimization of public transport vehicles and private vehicles according to claim 1, characterized in that, In S3, for two consecutive intersections and The road segments corresponding to the two intersections are respectively and Buses on the road and road sections The travel times are respectively and The signal period is The green light time is Public buses exist Arrival time section Starting point; intersection The green light interval is: [ , ]； intersection The green light interval is: [ , ]； in, For public transport vehicles Arrival Section The starting point time, It is a non-negative integer.

5. The green wave control method for coordinated optimization of public transport vehicles and private vehicles according to claim 1, characterized in that, In S4, private vehicles on the road section The average travel time on the platform is: ； in, Indicates road segment The average travel time for normal passage of social vehicles. Indicates road segment Upper The normal travel time for a private vehicle. Indicates road segment The total number of social vehicles that are normally passing through.

6. The green wave control method for coordinated optimization of public transport vehicles and private vehicles according to claim 1, characterized in that: In S6, the basic green wave scheme is combined with various adjustment schemes to obtain multiple green wave control schemes. The average travel time of social vehicles in the entire green wave band or the "person × vehicle speed" index is used as the evaluation index to select the optimal scheme. The PTV-VISSIM simulation software is used for simulation. By comparing the average travel time of social vehicles in the entire green wave band under each scheme, the scheme with the minimum average travel time is selected as the optimal scheme.

7. The green wave control method for coordinated optimization of public transport vehicles and private vehicles according to claim 1, characterized in that, The stopping time in S2 is dynamically calculated using the following formula: ； in, , These represent the number of people boarding and alighting at the platform, respectively. , These are the times required for a single passenger to board and alight, respectively. S5 departure interval With signal period satisfy: ； in, It is a positive integer.

8. The green wave control method for coordinated optimization of public transport vehicles and private vehicles according to claim 1, characterized in that, The objective function of the lower-level model is: ； in, The average cost of travel per passenger on public transport. , , These represent the total cost of waiting time for public transport passengers, the total cost of riding the bus, and the total cost of operating time for the intelligent bus fleet, respectively. For the first Passenger flow at each station; The objective function of the upper-level model is: ； in, This represents the total number of bus stops. For the first Station to the Total passenger flow between stations , These represent the passenger flow sharing ratios for public transportation and private vehicles, respectively. , These represent the speeds of public buses and private vehicles, respectively.

9. The green wave control method for coordinated optimization of public transport vehicles and private vehicles according to claim 1, characterized in that, If buses operate in platoons, then the minimum value of the green wave width in the upper-level model is... Determined based on the fleet size output by the lower-level model: ； in, This represents the number of vehicles in the convoy. This refers to the headway of the train. During the iteration of the upper and lower layer models, the passenger flow sharing ratio is updated using the Logit model based on the improvement of public transportation operation efficiency: ； ； in, , The travel utility of choosing public transportation and private vehicles is considered respectively. Based on this, the bus origin-destination (OD) demand of each station is updated and the next layer of optimization is carried out.

10. A green wave control system for coordinated optimization of public transport vehicles and private vehicles, used to implement the green wave control method for coordinated optimization of public transport vehicles and private vehicles as described in any one of claims 1-9, characterized in that, include: The information collection unit is used to obtain the actual pedestrian flow characteristics of bus stops on the road segment and the traffic flow characteristics of social vehicles on the road segment; The information processing unit is used to determine the frequency of bus departures and the stopping time of buses at the bus stop based on the actual passenger flow characteristics of the bus stop obtained by the information collection unit. Based on the traffic flow characteristics of social vehicles obtained by the information collection unit, the average travel time of social vehicles on the road segment is determined. And determine the intersection signal cycle based on the frequency of bus departures, and determine the travel time of buses on the road segment based on the stopping time of buses at bus stops and the travel time of buses on the road segment. The simulation decision-making unit is used to determine the green light intervals at each intersection based on the signal cycle determined by the information processing unit, the travel time of buses on each road segment, and the average travel time of private vehicles on each road segment, thus obtaining a basic green wave scheme for buses. Based on the basic green wave scheme, it generates multiple adjustment schemes by changing the offset of the green light interval at the next intersection within the signal cycle. The basic green wave scheme is combined with these adjustment schemes to obtain various green wave control schemes. Finally, the simulation module is invoked to evaluate these green wave control schemes, using the average travel time of private vehicles throughout the green wave segment or the "person x vehicle speed" index as the evaluation metric to select the optimal scheme from the multiple green wave control schemes. The implementation unit is used to connect to the traffic signal control system and run the optimal solution selected by the simulation decision unit.

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