Passenger exchange traffic flow channel traffic capacity assessment method, application and system

Abstract

The invention discloses a traffic capacity assessment method, application and system for a traffic flow channel with passenger exchange. The method comprises the following steps: abstracting a vehicle queuing system into a single-lane queuing system, simulating a vehicle queuing process by using an M / M / 1 queuing model controlled by a threshold value, and deducing to obtain a traffic flow model. And fitting a traffic flow model according to measured data, determining fitting parameters, and obtaining a macroscopic fundamental diagram and an optimal traffic flow density. And comparing the traffic flow density with the optimal traffic flow density, and when the traffic flow density is smaller than the optimal traffic flow density, releasing the vehicle to enter the vehicle queuing area. And when the traffic flow density is greater than the optimal traffic flow density, limiting the vehicles from entering the vehicle queuing area. The traffic flow model is constructed according to the single-lane queuing model controlled by the threshold value, the traffic capacity of the landside channel of the large hub can be dynamically adjusted, and the departure efficiency of passengers is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to a method for evaluating the traffic flow channel capacity with passenger exchange, a traffic control method based on the method for evaluating the traffic flow channel capacity, and a corresponding traffic flow channel capacity evaluation system, belonging to the technical field of traffic engineering. Background Art

[0002] Large-scale transportation hubs such as aviation and railways play a vital role in urban transportation. They not only connect inter-city transportation, but are also closely connected with intra-city transportation, and play a key role in passenger flow distribution. With the increasing pressure of urban traffic, optimizing and controlling the distribution capacity of hub traffic and passenger flow has become the focus of the operation department. In particular, the passenger exchange area is a key area connecting the hub's land-side automobile channel with the hub. The interaction between passengers and vehicles directly affects the hub's distribution capacity and also forms the unique traffic characteristics of the land-side channel.

[0003] In the landside corridor of a hub, the increase in traffic density leads to a rapid decrease in capacity, which is mainly due to the interaction between traffic and departing passenger flow. Theoretical studies have shown that the hysteresis of traffic state transition is the main reason for the decrease in capacity. This shows that the capacity of the landside corridor of a large hub cannot be simply determined by direct measurement.

[0004] At present, the description of traffic flow characteristics mainly relies on data fitting methods and traffic flow model derivation methods based on queuing theory. The traffic flow macro basic diagram provides an intuitive graphical representation of the traffic flow model by showing the relationship between traffic flow, speed and density. Existing traffic flow models include linear, logarithmic, exponential speed-density models, GHR_M2 model, Draw model and Edie model. These models have their own applicable scenarios, but they all have limitations. For example, the linear speed-density model is derived based on observation data under specific conditions, but it does not have wide applicability and cannot describe the characteristics of reduced road capacity. The logarithmic speed-density model is suitable for traffic congestion, but the speed value is too large in the free flow state, which does not conform to the actual situation. The exponential speed-density model is demonstrated under low traffic density. For traffic flow in forced flow or congestion, the calculated speed value deviates from the actual value. The GHR_M2 model and the Draw model increase the degree of freedom of the model by introducing additional parameters, but they still cannot fully describe the characteristics of reduced capacity. The Edie model combines logarithmic and exponential speed-density models, which, although applicable in some cases, also cannot accurately reflect the traffic flow characteristics unique to the landside corridors of large hubs.

[0005] In summary, both the classic traffic flow model based on data fitting and the model derived from queuing theory have failed to fully consider the state transition lag of traffic flow in the landside channel of the hub affected by passenger exchanges, making it difficult to accurately describe and estimate the channel's capacity. Summary of the invention

[0006] The primary technical problem to be solved by the present invention is to provide a method for evaluating the traffic capacity of a traffic channel with passenger exchange.

[0007] Another technical problem to be solved by the present invention is to provide a traffic control method based on the vehicle flow channel capacity evaluation method.

[0008] Another technical problem to be solved by the present invention is to provide a traffic channel capacity assessment system with passenger exchange.

[0009] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0010] According to a first aspect of an embodiment of the present invention, a method for evaluating the traffic capacity of a traffic channel with passenger exchange is provided, comprising the following steps:

[0011] Step S1: Setting the facility parameters of the passenger queuing area, vehicle queuing area and vehicle service area;

[0012] Step S2: abstract the vehicle queuing system into a single lane queuing system;

[0013] Step S3: using the threshold-controlled M / M / 1 queue model to simulate the queueing process of vehicles;

[0014] Step S4: deriving a traffic flow model;

[0015] Step S5: fitting the traffic flow model according to the measured data to determine the fitting parameters of the traffic flow model;

[0016] Step S6: Select the indicators MSE, SSE, MAE, RMSE and goodness of fit (R-square) as a basis to evaluate the fitting effect of the traffic flow model;

[0017] Step S7: obtaining a macro basic graph according to the fitted traffic flow model;

[0018] Step S8: Obtaining the optimal traffic density under actual conditions according to the macro basic map;

[0019] Step S9: obtaining the vehicle flow density of the vehicle queuing system;

[0020] Step S10: comparing the traffic density with the optimal traffic density; when the traffic density is less than the optimal traffic density, jumping to step S11; when the traffic density is greater than the optimal traffic density, jumping to step S12;

[0021] Step S11: issuing an early warning, allowing the vehicle to enter the vehicle queuing area, and jumping to step S9;

[0022] Step S12: Issue an early warning, restrict vehicles from entering the vehicle queuing area, and jump to step S9.

[0023] Preferably, the facility parameters provided in step S1 include the batch capacity N of the vehicle queuing area, the length I of the vehicle queuing area and the vehicle service area, the number of lanes m in the vehicle service area, the number of queues n in the passenger queuing area, and the vehicle free flow speed v f and channel traffic blocking density k j .

[0024] Preferably, the traffic flow model provided in step S4 is as follows:

[0025]

[0026] Where N is the batch capacity of the vehicle queuing area; q is the actual traffic volume, measured in number of vehicles; k is the traffic density of the vehicle queuing area and vehicle service area, measured in number of vehicles; v f is the free flow speed; k j is the channel traffic blockage density; D is the lower threshold of the queue length in the vehicle queuing area under the actual state, measured in the number of vehicles; U is the upper threshold of the queue length in the vehicle queuing area under the actual state, measured in the number of vehicles; π(i, j) represents the probability that the queue length is i in the queuing system in state j (j=0, 1), measured in vehicle batches; j=0 indicates that the system is in a non-congested state, and j=1 indicates a congested state.

[0027] Preferably, step S5 includes the following sub-steps:

[0028] S51: Determine fitting parameters;

[0029] S52: Obtaining measured traffic flow data according to predetermined statistical conditions and fitting a model.

[0030] Preferably, the fitting parameters provided by S51 include the service rate μ1 of the vehicle service area in a non-congested state, the service rate μ2 of the vehicle service area in a congested state, the low threshold D of the queue length of the vehicle queuing area and the high threshold U of the queue length of the vehicle queuing area.

[0031] According to a second aspect of an embodiment of the present invention, a traffic control method based on the above-mentioned vehicle flow channel capacity evaluation method is provided, which specifically includes the following steps:

[0032] Step S101: According to the actual landside channel conditions of large hubs, obtain facility parameters; wherein the facility parameters include the batch capacity N of the vehicle queuing area, the length 1 of the vehicle queuing area and the vehicle service area, the number of lanes m in the vehicle service area, the number of queues n in the passenger queuing area, and the vehicle free flow speed v f and channel traffic blocking density k j ;;

[0033] Step S102: Substituting the facility parameters into the following formula, fitting the service rate μ1 of the vehicle service area in a non-congested state, the service rate μ2 of the vehicle service area in a congested state, the low threshold D of the queue length of the vehicle queuing area, and the high threshold U of the queue length of the vehicle queuing area, to obtain a macro basic graph;

[0034]

[0035] Where N is the batch capacity of the vehicle queuing area; q is the actual traffic volume, measured in number of vehicles; k is the traffic density of the vehicle queuing area and vehicle service area, measured in number of vehicles; v f is the free flow speed; k j is the channel traffic blockage density; D is the lower threshold of the queue length in the vehicle queuing area under the actual state, measured in the number of vehicles; U is the upper threshold of the queue length in the vehicle queuing area under the actual state, measured in the number of vehicles; π(i, j) represents the probability that the queue length is i in the queuing system in state j (j=0, 1), measured in vehicle batches; j=0 indicates that the system is in a non-congested state, and j=1 indicates a congested state.

[0036] Step S103: According to the macro basic diagram, the optimal traffic density k under the actual state is obtained mm ;

[0037] Step S104: obtaining the vehicle flow density k in the vehicle queuing area;

[0038] Step S105: Compare the traffic density k with the optimal traffic density k mm ; When k<k mm When k≥k mm When , jump to step S107;

[0039] Step S106: issuing an early warning and equipment signal, allowing the vehicle to enter the vehicle queuing area, and jumping to step S104;

[0040] Step S107: Issue an early warning and equipment signal to restrict vehicles from entering the vehicle queuing area, and jump to step S104.

[0041] According to a third aspect of an embodiment of the present invention, a vehicle flow channel capacity assessment system with passenger exchange is provided, which is used to implement the above-mentioned vehicle flow channel capacity assessment method, and specifically includes a vehicle flow density acquisition module, a capacity assessment module and a signal output module; wherein,

[0042] The vehicle flow density acquisition module is used to obtain the vehicle flow density k and transmit it to the traffic capacity evaluation module;

[0043] The traffic capacity evaluation module stores the optimal vehicle flow density k mm The value of k mm The value of is obtained according to the above-mentioned vehicle channel capacity evaluation method; the capacity evaluation module is used to compare k and k mm ;

[0044] The signal output module issues warning and equipment signals according to the comparison result of the traffic capacity evaluation module.

[0045] Preferably, the vehicle flow channel capacity assessment system further includes a parameter input module; wherein,

[0046] The parameter input module is used to input facility parameters into the traffic capacity assessment module;

[0047] The traffic capacity evaluation module calculates the optimal traffic density k according to the above-mentioned traffic channel traffic capacity evaluation method. mm and compare k and k mm .

[0048] Compared with the prior art, the vehicle channel capacity assessment method and system provided by the embodiment of the present invention, by adopting the M / M / 1 queuing model controlled by a threshold value, effectively solves the limitation of the existing traffic flow model in describing specific traffic conditions, especially the characteristic of decreased capacity when the traffic density increases. The method can dynamically compare the real-time traffic density data with the optimal traffic density, and intelligently control the vehicles entering the queuing area, thereby optimizing the capacity of the landside channel of a large transportation hub and significantly improving the departure efficiency of passengers. In addition, the present invention integrates multiple modules such as traffic density collection, capacity assessment and signal output, realizes the automation and intelligence of the assessment process, enhances the system adaptability and application scope, and can be widely used in different traffic management and control scenarios. In general, the present invention not only improves the scientificity and accuracy of traffic decision-making, but also provides strong technical support for reducing traffic congestion and improving traffic flow efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of a vehicle queuing system and a passenger queuing system for a landside channel of a large hub in the first embodiment of the present invention;

[0050] Figure 2 for Figure 1 The vehicle queuing system in is abstracted as a schematic diagram of a single-lane queuing system;

[0051] Figure 3 For the simulation of the threshold-controlled M / M / 1 queue model Figure 2 Schematic diagram of a single lane queuing system in ;

[0052] Figure 4 It is a schematic diagram of the process of obtaining a macro basic graph in the first embodiment of the present invention. DETAILED DESCRIPTION

[0053] The technical content of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] The technical concept in the embodiment of the present invention is: using a threshold-controlled queuing model to simulate the queuing process of vehicles in the landside channel of the transportation hub, obtaining a traffic flow model and a macro basic diagram to estimate the traffic capacity of the landside channel, and then perform traffic control to obtain the maximum traffic efficiency. Specifically, the vehicle queuing system of the landside channel is abstracted into a single-lane queuing system, and the M / M / 1 queuing model controlled by the threshold is used to simulate the queuing process of vehicles in the channel to obtain the ideal traffic density at the maximum traffic flow, and the ideal traffic density is used as the benchmark for controlling the traffic capacity of the roadside traffic channel.

[0055] like Figure 1 and Figure 2 As shown, the landside passage and passenger exchange area of ​​the transportation hub in the embodiment of the present invention include a passenger queuing area, a vehicle queuing area and a vehicle service area. Among them, vehicles refer to taxis and online-hailing cars that carry passengers to leave the station at the transportation hub, and are characterized by entering the vehicle queuing area from the urban roads around the hub to wait, entering the lanes of the vehicle service area in batches, and leaving the hub in batches after picking up passengers. The vehicle queuing area is the area where vehicles queue up and wait before entering the vehicle service area, and is also the storage pool of this vehicle queuing system. Passengers enter the passenger queuing area from the hub, queue up in queues, and enter the vehicle service area in batches to take vehicles to leave the hub. The passenger queuing area is the area where passengers queue up and wait before entering the vehicle service area. The vehicle service area is the area where vehicles pick up passengers and passengers take vehicles. The landside passage provided in the embodiment of the present invention includes a vehicle queuing area and a vehicle service area, and the passenger exchange area provided includes a passenger queuing area and a vehicle service area.

[0056] It is worth noting that vehicles enter the vehicle service area in batches, which means that the next batch of vehicles enters the vehicle service area after the previous batch of vehicles have left with passengers. The vehicle service area includes multiple lanes, and each batch of vehicles evenly occupies each lane of the vehicle service area, that is, each lane includes one or more vehicles, and the number of vehicles in each lane is the same, and the number of vehicles in each batch is also the same. Therefore, the queuing model provided in the embodiment of the present invention can be abstracted into a single-lane queuing system.

[0057] First embodiment

[0058] An embodiment of the present invention provides a method for evaluating the traffic capacity of a traffic channel with passenger exchange, which specifically includes the following steps.

[0059] Step S1: Setting the facility parameters of the passenger queuing area, vehicle queuing area and vehicle service area.

[0060] The facility parameters include the batch capacity N of the vehicle queuing area, the length 1 of the vehicle queuing area and the vehicle service area, the number of lanes m in the vehicle service area, the number of queues n in the passenger queuing area, and the vehicle free flow speed v f and channel traffic blocking density k j .

[0061] Specifically, in the passenger queue area, vehicle queue area and vehicle service area, there are two capacity-limited queue systems, namely, the passenger queue system and the vehicle queue system. Among them, the passenger queue system exists in the passenger exchange area, and the vehicle queue system exists in the vehicle queue area and the vehicle service area. Free flow speed v f That is, there is only one group of vehicles in the entire vehicle queuing area and vehicle service area. The speed of this group of vehicles entering the vehicle queuing system, driving directly from the vehicle queuing area to the vehicle service area to pick up passengers and then leaving.

[0062] A flight or railway train with a speed of λ a After arriving at the hub, passengers drop off at a rate of λ p Arriving at the passenger queuing area, there are n queues in the passenger queuing area. The vehicle service area has m lanes. Vehicles drive from the vehicle queuing area to the vehicle service area in batches, and leave in batches after picking up passengers. Subsequent batches of vehicles queue in the vehicle queuing area with a maximum batch capacity of N.

[0063] Step S2: Abstract the vehicle queuing system into a single-lane queuing system.

[0064] like Figure 2 As shown in Figure 1, because the vehicles in the queuing system carry passengers in batches, the actual vehicle queuing system can be abstracted into a single-lane queuing system. There is a conversion relationship between the traffic flow parameters of the actual vehicle queuing system and the traffic flow parameters of the abstract vehicle queuing system.

[0065] Specifically, the relationship between the traffic flow parameters of the actual vehicle queuing system and the traffic flow parameters of the abstract single-lane vehicle queuing system is shown in formula (1).

[0066]

[0067] The actual vehicle queuing system (in terms of the number of vehicles) has a traffic volume of q, a traffic density of k, and a blocking density of k. j , the number of queues in the passenger queuing area is n, and the number of lanes is m. The traffic flow of the abstract vehicle queuing system (in vehicle batches) is q b , the traffic density is k b , the blocking density is k bj .

[0068] For the vehicle queuing system, vehicles pick up passengers in the vehicle service area, and vehicles queue in the vehicle queuing area. The process of each batch of vehicles arriving at the vehicle queuing area is set as a Poisson process, and the parameter is λ b The exponential distribution of each batch of vehicles arriving at the vehicle queue area is λ b ). The service rate of vehicles picking up passengers in the vehicle service area is μ b , which also obeys the exponential distribution. Based on the characteristics of the human-vehicle interaction process, the vehicle queuing system has two states, namely, non-congested state and congested state. The transition between the two states is controlled by the length of the vehicle queue, and the service rate μ of vehicles picking up passengers in the vehicle service area under different states is b The value of is different.

[0069] According to the abstraction of the vehicle queuing system, the vehicle queuing system provided in the embodiment of the present invention is abstracted as a single-lane queuing system. The retention time E of each batch of vehicles in the vehicle queuing system b (S) includes the waiting time in the vehicle queuing area and the time to pick up passengers in the vehicle service area. When the number of vehicle batches in the vehicle queuing area reaches N, the traffic flow in the landside channel is blocked. At this time, the traffic density (in vehicle batches) is the blocking density k bj According to the definition of blocking density, 1 / k bj is the minimum road section length required for each batch of vehicles, that is, the length of the vehicle service area. The time consumed by each batch of vehicles to completely pass through the vehicle service area is E b (S), so the speed v of each batch of vehicles in the landside channel is as shown in formula (2):

[0070]

[0071] Where v is the speed of each batch of vehicles passing through the landside channel, E b(S) is the time it takes for each batch of vehicles to completely pass through the vehicle service area.

[0072] Step S3: Use the threshold-controlled M / M / 1 queuing model to simulate the queuing process of vehicles.

[0073] like Figure 3 As shown in Figure 1, the threshold-controlled M / M / 1 queueing model is used to simulate the queueing process of vehicles in the landside channel. Based on the M / M / 1 threshold queueing model, the vehicle queueing system is modeled. The system has two states: non-congested and congested. The transition between the two states is subject to a low threshold (in vehicle batches) D b and high threshold (in vehicle batches) U b Control: ① The number of vehicle batches queuing in the vehicle queuing area (the length of the queue, referred to as the captain) is less than D b In the stage, the service rate of the vehicle service area to pick up passengers is higher, subject to the parameter μ b1 exponential distribution; ② The number of vehicle batches queuing in the vehicle queuing area is greater than U b In the stage, the service rate of the vehicle service area is low, subject to the parameter μ b2 exponential distribution; ③ The number of vehicle batches queuing in the vehicle queuing area is between (D b , U b ) between the vehicle service area and μ b1 , μ b2 Two service rates.

[0074] Specifically, according to the characteristics of the threshold-controlled queue system, each batch of vehicles is b The average interval time of arriving at the vehicle queuing area and gradually forming a queue is divided into three stages: ① In the initial stage, the traffic volume is small, the traffic in the vehicle queuing area is in a non-congested state, and the vehicles leave quickly with passengers. The average time consumed by each batch of vehicles in the vehicle service area is 1 / μ b1 ; ② As the traffic volume increases, the length of the vehicle queue in the vehicle queuing area gradually increases, and the number of batches of vehicles waiting to queue exceeds the high threshold U b After that, it means that the traffic in the vehicle queuing system is in a congested state. At this stage, the time for each batch of vehicles to pick up passengers and leave the vehicle service area is also extended to 1 / μ b2 ; ③ As the number of passengers and vehicles continues to dissipate, the length of the queue in the vehicle queuing area gradually shortens, and the number of vehicle batches in the queue decreases to a low threshold D b After that, the traffic in the vehicle service area is in a non-congested state again, and the average time spent by vehicles in the vehicle service area is reduced back to 1 / μ b1 .

[0075] Step S4: derive a traffic flow model.

[0076] Specifically, according to the relevant theories of Markov chains, the average retention time of vehicles (batches) in the queuing system when the system reaches a steady state is obtained. Let π(i, j) represent the probability that the queue length (in terms of vehicle batches) of the queuing system is i in state j (j = 0, 1), j = 0 represents the system is in a non-congested state, and j = 1 represents a congested state. The load rate of the vehicle service area in the non-congested state is ρ b1 =λ b / μ b1 , the load rate under congestion condition ρ b2 =λ b / μ b2 , then the steady-state probability of the system can be obtained by equations (3) to (6).

[0077] π(i,0)=π(0,0)ρ b1 i , i = 1, 2, ..., D b -1 (3)

[0078]

[0079] Where π(0, 1) can be obtained according to the regularity condition shown in formula (7):

[0080]

[0081] According to Little's formula, the average retention time of each batch of vehicles in the vehicle queuing system is:

[0082]

[0083] Among them, the effective arrival rate of the vehicle queuing system is Λ.

[0084] and:

[0085]

[0086] From equation (2) and equation (8) to equation (12), we can obtain equation (13).

[0087] q b =k b ·v (10)

[0088] λ b =k b ·v f (11)

[0089] k b =(1-π(0,0))k bj (12)

[0090]

[0091] like Figure 4 As shown in Figure 1, due to the abstraction of the traffic flow in the channel, the vehicles in the queuing system are based on batches as the basic unit, and the variables and parameters in the traffic flow model (13) derived based on the queuing model are also based on batches as the basic unit. However, the actual observed traffic flow data are based on vehicles as the basic unit. Therefore, it is necessary to convert formula (13) into formula (14) according to formula (1):

[0092]

[0093] Where N is the batch capacity of the vehicle queuing area. q is the actual vehicle flow, measured in number of vehicles. k is the actual vehicle flow density in the vehicle queuing area and vehicle service area, measured in number of vehicles. f is the free flow velocity. k j is the traffic congestion density under actual conditions. D is the lower threshold of the queue length in the vehicle queue area under actual conditions, measured in the number of vehicles. U is the upper threshold of the queue length in the vehicle queue area under actual conditions, measured in the number of vehicles.

[0094] Step S5: Fit the model according to the measured data to determine the fitting parameters of the traffic flow model.

[0095] The parameters μ1, μ2, D, U and μ contained in formula (14) b1 , μ b2 , D b , U b The conversion relationship is shown in formula (15).

[0096] μ b1 =μ1 / (n·m),μ b2 =μ2 / (n·m), D b =D / (n·m),U b =U / (n·m) (15)

[0097] Wherein, μ1 is the service rate of the vehicle service area in the non-congested state under the actual state, measured in the number of vehicles. μ2 is the service rate of the vehicle service area in the congested state under the actual state, measured in the number of vehicles. The probability π(0, 1) that the system transitions from the non-congested state to the congested state depends on the service rate μ1 and the service rate μ2. b1 μ is the service rate of the vehicle service area in a non-congested state under the abstract single-lane state, measured in vehicle batches. b2 It is the service rate of the vehicle service area in a congested state under an abstract single-lane state, measured in vehicle batches.

[0098] Fitting the parameters of the traffic flow model includes the following sub-steps.

[0099] S51: Determine fitting parameters.

[0100] The parameters included in formula (14) are k j 、v f , μ1, μ2, D and U. Since k bj It can be determined by N and l, that is, 1 / k bj =1 / (N+1), according to k as shown in formula (1) j and k bj The relationship between k j As shown in formula (16).

[0101]

[0102] Among them, the length 1 of the vehicle queuing area and the vehicle service area can be measured according to the actual situation. In addition, v f It can also be obtained through actual measurement. Therefore, k j and v f All are definable parameters.

[0103] Key facility parameters in the vehicle queuing system and the passenger queuing system include the number of queues n in the passenger queuing area, the number of lanes m in the vehicle service area, the capacity N of the vehicle queuing area, and the channel traffic blockage density k j , will affect the fitting effect. This set of parameters can be directly obtained according to the actual situation. Therefore, the parameters that need to be fitted in formula (14) are μ1, μ2, D and U.

[0104] S52: Obtaining measured traffic flow data according to predetermined statistical conditions and fitting a model.

[0105] Based on this, multiple groups of vehicle flow, density and speed data in the landside channel of a large hub are obtained. Based on the measured data, equation (14) is fitted to determine the fitting parameter values.

[0106] Step S6: Select the indicators MSE, SSE, MAE, RMSE and goodness of fit (R-square) as the basis to evaluate the fitting effect.

[0107] For example, the fitting data is divided into three regions, namely, the low density region before reaching the maximum flow (0≤k<k mm ), the medium density area reaching the maximum flow part (k mm ≤k<k j ) and high-density areas where the flow rate drops rapidly (k ≥ k j ). The goodness of fit of the model in each region and as a whole, as well as various evaluation indicators, were calculated to verify that the model had a good fitting effect.

[0108] Step S7: Obtain a macro basic graph according to the fitted model.

[0109] The macro basic diagram depicts the relationship between the three basic parameters of traffic flow - traffic density, traffic volume and average speed.

[0110] Step S8: According to the obtained macro basic map, the optimal traffic density under the actual state is obtained.

[0111] Ideally, as the traffic density increases, the traffic volume will initially increase because there are more vehicles in the channel. However, when the traffic density reaches a certain critical point (the so-called "optimal traffic density"), the traffic volume begins to decrease. This is because the vehicles are too dense, resulting in a decrease in speed and eventually congestion. This critical point corresponds to the maximum traffic volume of the landside channel, that is, the traffic capacity of the landside channel. The optimal traffic density provided by the embodiment of the present invention is k mm .

[0112] Step S9: Obtain the vehicle flow density of the vehicle queuing system.

[0113] Step S10: Compare the traffic density of the vehicle queuing system with the optimal traffic density; when the traffic density is less than the optimal traffic density, jump to step S11; when the traffic density is greater than the optimal traffic density, jump to step S12.

[0114] Step S11: Issue an early warning, allow the vehicle to enter the vehicle queuing area, and jump to step S9.

[0115] Step S12: Issue an early warning, restrict vehicles from entering the vehicle queuing area, and jump to step S9.

[0116] Second embodiment

[0117] A traffic control method, based on the model in the first embodiment, evaluates the traffic flow operation status of the landside channel of a large hub and implements traffic control, specifically including steps S101 to S106.

[0118] Step S101: According to the actual landside channel conditions of large hubs, obtain facility parameters; wherein the facility parameters include the batch capacity N of the vehicle queuing area, the length 1 of the vehicle queuing area and the vehicle service area, the number of lanes m in the vehicle service area, the number of queues n in the passenger queuing area, and the vehicle free flow speed v f and channel traffic density k j .

[0119] The embodiment of the present invention takes the following parameter values ​​as an example: N=165, l=700m, m=2, n=3, v f =200m / min, k j =1.42veh / m.

[0120] Step S102: Substitute the above-mentioned facility parameters into formula (14), and use the measured traffic flow data to fit and determine the service rate μ1 of the vehicle service area in a non-congested state, the service rate μ2 of the vehicle service area in a congested state, the low threshold D of the queue length of the vehicle queuing area, and the high threshold U of the queue length of the vehicle queuing area, and obtain the macro basic map.

[0121] The embodiment of the present invention takes the following statistical conditions to obtain measured traffic flow data as an example: the statistical period is 7 days without holidays, the statistical interval is 10 minutes, the traffic density is counted once every 20 seconds within the statistical interval, and the average value is taken as the statistical traffic density k within the statistical interval.

[0122] The embodiment of the present invention takes the following parameter values ​​obtained by fitting as an example: μ1=452.2, μ2=266.6, D=577, U=816.

[0123] The embodiment of the present invention takes the following evaluation index values ​​as an example:

[0124]

[0125] Step S103: Based on the above macro basic diagram, the optimal traffic density k under the actual state is obtained. mm .

[0126] The embodiment of the present invention takes the following parameter values ​​as an example: mm =0.96veh / m.

[0127] Step S104: Obtain the vehicle flow density k in the vehicle queuing area.

[0128] It is worth noting that the method for obtaining the traffic density k includes analyzing the monitoring images obtained by the camera device. According to the video images collected by the camera device, the traffic density of the road in the monitoring video is determined. This part is a well-known technology and will not be repeated here.

[0129] Step S105: Compare the traffic density k with the optimal traffic density k mm ; When k<k mm When k≥k mm When , jump to step S107.

[0130] Step S106: Send out an early warning and equipment signal, allow the vehicle to enter the vehicle queuing area, and jump to step S104.

[0131] Step S107: Issue an early warning and equipment signal to restrict vehicles from entering the vehicle queuing area, and jump to step S104.

[0132] The purpose of restricting vehicles from entering the vehicle queuing area is to mmWhen the traffic density k increases, the traffic flow q gradually decreases, and the traffic capacity of the landside channel decreases. Restricting vehicles from entering the vehicle queuing area and reducing the traffic density of the vehicle queuing system can restore the traffic capacity of the landside channel.

[0133] In the example provided in the embodiment of the present invention, when k<0.96veh / m, the vehicle is allowed to enter the vehicle queuing area. When k≥0.96veh / m, the vehicle is restricted from entering.

[0134] Third embodiment

[0135] The embodiment of the present invention provides a vehicle flow channel capacity assessment system with passenger exchange, including a vehicle flow density acquisition module, a capacity assessment module and a signal output module.

[0136] The vehicle flow density acquisition module is used to obtain the vehicle flow density k and transmit it to the traffic capacity evaluation module.

[0137] The traffic capacity evaluation module stores the optimal traffic density k mm The value of k mm The value of is obtained by the vehicle channel capacity evaluation method described in the first embodiment. The capacity evaluation module compares k and k mm size.

[0138] When k < k mm When the vehicle enters the queuing area, the signal output module sends out an early warning and equipment signal to allow the vehicle to enter the queuing area.

[0139] When k ≥ k mm When the vehicle enters the queuing area, the signal output module sends out a warning and equipment signal to restrict vehicles from entering the queuing area.

[0140] Fourth embodiment

[0141] Different from the above-mentioned embodiment, the vehicle flow channel capacity evaluation system further includes a parameter input module, which is used to input facility parameters into the capacity evaluation module.

[0142] The traffic capacity evaluation module calculates the optimal traffic density k according to the traffic channel traffic capacity evaluation method described in the first embodiment. mm And compare k and k mm size.

[0143] Fifth embodiment

[0144] Different from the second embodiment, the traffic control method provided in the embodiment of the present invention can also be applied to the engineering practice of manned or unmanned express delivery vehicles queuing for loading at express delivery stations, which specifically includes steps S201 to S206.

[0145] Step S201: According to the actual situation of the actual express loading station, the facility parameters are obtained; wherein the facility parameters include the batch capacity N of the express vehicle queuing area, the length 1 of the express vehicle queuing area and the express vehicle service area, the number of lanes m of the express vehicle service area, the number of queues n of the express package delivery area, and the free flow speed v of the express vehicle. f and channel express traffic blocking density k j .

[0146] Step S202: Substitute the above-mentioned facility parameters into formula (14), fit the service rate μ1 of the express vehicle service area under non-congested conditions, the service rate μ2 of the express vehicle service area under congested conditions, the low threshold D of the queue length of the express vehicle queuing area, and the high threshold U of the queue length of the express vehicle queuing area, and obtain the macro basic diagram.

[0147] Step S203: Based on the above macro basic graph, the optimal express vehicle flow density k under actual conditions is obtained. mm .

[0148] Step S204: Obtain the express vehicle flow density k in the express vehicle queuing area.

[0149] Step S205: Compare the express delivery traffic density k with the optimal express delivery traffic density k mm ; When k<k mm When k≥k mm When , jump to step S207.

[0150] Step S206: Issue an early warning and equipment signal to allow the express vehicle to enter the express vehicle queuing area, and jump to step S204.

[0151] Step S207: Issue an early warning and equipment signal to restrict express vehicles from entering the express vehicle queuing area, and jump to step S204.

[0152] Sixth embodiment

[0153] Different from the second embodiment, the traffic control method provided in the embodiment of the present invention can also be applied to the engineering practice of loading bulk cargo into transport vehicles. For example, the traffic flow control of loading bulk solid cargo such as coal ore into multiple trucks, and the traffic flow control of loading liquid industrial products such as petroleum and its derivatives into multiple tank trucks.

[0154] The traffic flow models obtained by using different queuing models are different. Common queuing models include M / M / 1 model, M / G / 1 model and G / G / 1 model.

[0155] Among them, the customer arrival and service time of the M / M / 1 model follow the Poisson process, that is, the arrival rate and service rate both follow the exponential distribution, which conforms to the properties of the Markov process. The customer arrival of the M / G / 1 model follows the Poisson process, while the service time follows the general distribution, that is, the service time can be arbitrarily distributed. The customer arrival and service time of the G / G / 1 model both follow the general distribution, which is neither a specific Poisson process, but an arbitrary distribution.

[0156] In the engineering scenario where the embodiment of the present invention is applied, the process of each batch of vehicles arriving at the vehicle service area and the process of vehicles picking up passengers at the vehicle service area are Poisson processes and obey exponential distribution, so the embodiment of the present invention establishes a queuing model using the M / M / 1 model.

[0157] There are two capacity-limited queue systems in the landside channel and passenger interaction area of ​​large hubs, namely, taxi queues for passengers and passengers queues for rides. Therefore, it is more reasonable to study the traffic characteristics in the channel based on queueing theory. The hysteresis of traffic state conversion is the reason for the decline in traffic capacity. The threshold-controlled queue system is a type of queue system with variable service rate. There are two queue length thresholds, one low and one high. In the early stage, the queue is in a non-crowded state and the service rate of the service desk is high. When the queue length continues to increase and exceeds the high threshold, the queue is in a crowded state and the service rate of the service desk decreases. When the queue length gradually decreases to below the low threshold, the service desk resumes the high service rate again. When the queue length is between the low threshold and the high threshold, the service desk has both high and low service rates, which indicates that it takes some time for the service desk to restore the high service rate and there is hysteresis in the recovery of the service rate. Inspired by this, the threshold-controlled queue model is used to simulate the traffic queuing process in the landside channel of the hub, and the traffic flow model or macro basic diagram is obtained, which provides a theoretical basis for the estimation of channel capacity.

[0158] In summary, the embodiment of the present invention provides a method, application and system for evaluating the capacity of a traffic channel with passenger exchange, which constructs a traffic flow model through a single lane queuing model controlled by a threshold value to obtain a macro basic graph. Based on the macro basic graph, the capacity of the landside channel of a large hub is dynamically adjusted to effectively ensure the departure efficiency of passengers.

[0159] It should be noted that the above-mentioned multiple embodiments are only examples, and the technical solutions of various embodiments can be combined, all of which are within the protection scope of the present invention.

[0160] The order of the steps of the present invention can be changed according to actual needs, and the order of the steps can be changed, and is not limited to the order of the steps listed in the embodiments.

[0161] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0162] The above is a detailed description of the vehicle flow channel capacity assessment method, application and system with passenger exchange provided by the present invention. For those skilled in the art, any obvious changes made to it without departing from the essence of the present invention will constitute an infringement of the patent right of the present invention and will bear corresponding legal liabilities.

Claims

1. A method for evaluating the capacity of a traffic channel with passenger exchange, characterized in that The following steps are involved: Step S1: Setting the facility parameters of the passenger queuing area, vehicle queuing area and vehicle service area; Step S2: abstract the vehicle queuing system into a single lane queuing system; Step S3: using the threshold-controlled M / M / 1 queue model to simulate the queueing process of vehicles; Step S4: deriving a traffic flow model; Step S5: fitting the traffic flow model according to the measured data to determine the fitting parameters of the traffic flow model; Step S6: evaluating the fitting effect of the traffic flow model; Step S7: obtaining a macro basic graph according to the fitted traffic flow model; Step S8: Obtaining the optimal traffic density under actual conditions according to the macro basic map; Step S9: obtaining the vehicle flow density of the vehicle queuing system; Step S10: comparing the traffic density with the optimal traffic density; when the traffic density is less than the optimal traffic density, jumping to step S11; when the traffic density is greater than the optimal traffic density, jumping to step S12; Step S11: issuing an early warning, allowing the vehicle to enter the vehicle queuing area, and jumping to step S9; Step S12: Issue an early warning, restrict vehicles from entering the vehicle queuing area, and jump to step S9.

2. The method for evaluating the traffic channel capacity according to claim 1, characterized in that: The facility parameters provided in step S1 include the batch capacity N of the vehicle queuing area, the length I of the vehicle queuing area and the vehicle service area, the number of lanes m in the vehicle service area, the number of queues n in the passenger queuing area, and the vehicle free flow speed v f and channel traffic blocking density k j .

3. The method for evaluating the traffic channel capacity according to claim 1, characterized in that The traffic flow model provided in step S4 is shown in the following formula: Where N is the batch capacity of the vehicle queuing area; q is the actual traffic volume, measured in number of vehicles; k is the traffic density of the vehicle queuing area and vehicle service area, measured in number of vehicles; v f is the free flow speed; k j is the channel traffic congestion density; D is the lower threshold of the queue length in the vehicle queuing area under the actual state, measured in the number of vehicles; U is the upper threshold of the queue length in the vehicle queuing area under the actual state, measured in the number of vehicles; π(i,j) represents the probability that the queue length is i in the queuing system in state j (j=0,1), measured in vehicle batches; j=0 indicates that the system is in a non-congested state, and j=1 indicates a congested state.

4. The method for evaluating the traffic channel capacity according to claim 1, characterized in that The step S5 comprises the following sub-steps: S51: Determine fitting parameters; S52: Obtaining measured traffic flow data according to predetermined statistical conditions and fitting a model.

5. The method for evaluating the traffic channel capacity according to claim 4, characterized in that: The fitting parameters provided by S51 include the service rate μ1 of the vehicle service area in a non-congested state, the service rate μ2 of the vehicle service area in a congested state, the low threshold D of the queue length of the vehicle queuing area, and the high threshold U of the queue length of the vehicle queuing area.

6. A traffic control method, implemented based on the vehicle channel capacity assessment method according to any one of claims 1 to 5, characterized in that The following steps are involved: Step S101: According to the actual landside channel conditions of a large hub, obtain facility parameters; wherein the facility parameters include the batch capacity N of the vehicle queuing area, the length I of the vehicle queuing area and the vehicle service area, the number of lanes m in the vehicle service area, the number of queues n in the passenger queuing area, and the vehicle free flow speed v f and channel traffic blocking density k j ; Step S102: Substituting the facility parameters into the following formula, fitting the service rate μ1 of the vehicle service area in a non-congested state, the service rate μ2 of the vehicle service area in a congested state, the low threshold D of the queue length of the vehicle queuing area, and the high threshold U of the queue length of the vehicle queuing area, to obtain a macro basic graph; Where N is the batch capacity of the vehicle queuing area; q is the actual traffic volume, measured in number of vehicles; k is the traffic density of the vehicle queuing area and vehicle service area, measured in number of vehicles; v f is the free flow speed; k j is the channel traffic congestion density; D is the lower threshold of the queue length in the vehicle queue area under the actual state, measured in the number of vehicles; U is the upper threshold of the queue length in the vehicle queue area under the actual state, measured in the number of vehicles; π(i,j) represents the probability that the queue length of the queuing system is i in state j (j=0,1), measured in vehicle batches; j=0 indicates that the system is in a non-congested state, and j=1 indicates a congested state; Step S103: According to the macro basic diagram, the optimal traffic density k under the actual state is obtained mm ; Step S104: obtaining the vehicle flow density k in the vehicle queuing area; Step S105: Compare the traffic density k with the optimal traffic density k mm ; When k<k mm When k≥k mm When , jump to step S107; Step S106: issuing an early warning and equipment signal, allowing the vehicle to enter the vehicle queuing area, and jumping to step S104; Step S107: Issue an early warning and equipment signal to restrict vehicles from entering the vehicle queuing area, and jump to step S104.

7. A vehicle flow channel capacity assessment system with passenger exchange, characterized in that It includes a vehicle flow density acquisition module, a traffic capacity assessment module and a signal output module; among which, The vehicle flow density acquisition module is used to obtain the vehicle flow density k and transmit it to the traffic capacity evaluation module; The traffic capacity evaluation module stores the optimal vehicle flow density k mm The value of k mm The value of is obtained by the vehicle flow channel capacity evaluation method according to any one of claims 1 to 5; the capacity evaluation module is used to compare k and k mm ; The signal output module issues warning and equipment signals according to the comparison result of the traffic capacity evaluation module.

8. The vehicle channel capacity assessment system according to claim 7, characterized in that It also includes a parameter input module; wherein, The parameter input module is used to input facility parameters into the traffic capacity assessment module; The traffic capacity evaluation module calculates the optimal traffic density k according to the traffic channel traffic capacity evaluation method according to any one of claims 1 to 5. mm and compare k and k mm .

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