Heterogeneous bicycle team evolution state estimation method

Abstract

The invention discloses a heterogeneous bicycle team evolution state estimation method, and relates to the technical field of traffic management control. The heterogeneous bicycle team evolution state is described based on the perception density and pseudo lane theory, firstly, the operation characteristics of heterogeneous bicycle traffic flow on urban roads are deeply analyzed, the theory is integrated to a traffic flow cellular transmission model architecture, and a perception density and pseudo lane cellular transmission model considering the heterogeneous bicycle traffic flow characteristics is established; and then, through model verification, carrying out scientific comparison evaluation on the established model, a classical Robertson discrete model and a CTM model. The model can fully describe the evolution law of the hybrid bicycle fleet under the heterogeneous traffic flow condition, and has important guiding significance for lane planning and signal timing optimization in the hybrid bicycle environment.

Description

Technical Field

[0001] The invention relates to the technical field of traffic management and control, and in particular to a method for estimating the evolutionary state of a heterogeneous bicycle team. Background Art

[0003] Referring to the "Non-motor vehicle state estimation method and device, storage medium, and electronic device" with the publication number "CN111898475A", it can be seen that this patent solves the problem of improving the accuracy of non-motor vehicle state recognition, thereby achieving the effect of improving recognition accuracy;

[0004] During the implementation process, the applicant discovered that the application had the following technical problems:

[0005] The model is simply built using a self-learning approach, and most studies only consider a single vehicle flow, studying bicycle traffic flow as a homogeneous traffic flow. However, due to differences in bicycle types, speeds, and other aspects, there are infinite possibilities for mutual influence between different traffic flows. Ignoring this influence makes the model lack authenticity.

[0006] Currently, the fleet discrete model and cellular transmission model can be used to study the operating status of motor vehicle fleets, which are often used to study motor vehicle traffic flow. The classic fleet discrete model is difficult to apply to the study of bicycle traffic flow, and few studies have considered the heterogeneity of mixed bicycles.

[0007] Therefore, a new solution to the above problems needs to be proposed. Summary of the invention

[0008] The purpose of the present invention is to provide a method for estimating the evolutionary state of a heterogeneous bicycle team to solve the technical problems raised in the background technology.

[0009] To achieve the above object, the present invention provides the following technical solution: a method for estimating the evolution state of a heterogeneous bicycle team, comprising at least the following steps:

[0010] S1: Based on the perceived density, the heterogeneity of mixed bicycle traffic flow is described, bicycle traffic types are divided, the spatial allocation and perceived density expressions under three mixed bicycle traffic flow states are obtained, and the spatial ratio and perceived density are used to explain the segmentation of non-motorized vehicle roads;

[0011] S2: Based on the pseudo lane hypothesis cell division, the three states of spatial ratio and perceived density are described more intuitively;

[0012] S3: Based on the perception density expression and combined with the pseudo lane hypothesis theory, a model for estimating the evolution state of a heterogeneous bicycle team is established.

[0013] S4: Through data collection and analysis and parameter estimation, the outgoing flow at the upstream intersection and the arriving flow at the downstream intersection are analyzed, and then the evolution state of the heterogeneous bicycle team is estimated to obtain the prediction result of the mixed bicycle traffic flow;

[0014] S5: The constructed model is compared with the classic fleet discrete model Robertson model and the traditional CTM model to evaluate the advantages of the model of the present invention.

[0015] Furthermore, the S1 at least comprises the following steps:

[0016] The ordinary bicycle traffic flow in the non-motorized vehicle lane is set as traffic flow 1, the bicycle-type electric bicycle traffic flow is set as traffic flow 2, and the scooter-type electric bicycle traffic flow is set as traffic flow 3; the densities are k1, k2, and k3 respectively, and α1, α2, and α3 are used to represent the coefficients of the movable physical space of traffic flow 1, traffic flow 2, and traffic flow 3, respectively, and α1+α2+α3≤1;

[0017] The mutual influence between different types of mixed bicycles is characterized by density, and the perceived density is used to explain the road segmentation. The other types of vehicles around traffic flow 1 are regarded as traffic flow 1 with converted density, and the following formula is obtained:

[0018]

[0019] Taking traffic flow 1 as an example, if there is no traffic flow 1, its equivalent density and spatial share are zero;

[0020] When there is traffic flow 1, the spatial density perceived by traffic flow 1 is The perceived density of traffic flow 2 converted to traffic flow 1 is The perceived density of traffic flow 3 converted to traffic flow 1 is in Depends on the space conversion factor, The physical meaning of is that the traffic conditions of traffic flow 2 and traffic flow 3 are the same as those of traffic flow 1, that is, the density of other types of traffic flows and the density of traffic flow 1;

[0021] Traffic flow 1 converts all traffic flow 2 into the same type of traffic flow, and traffic flow 1 converts all traffic flow 3 into the same type of traffic flow, and occupies a certain part of the total space to affect the movement of traffic flow 1;

[0022] Conversion Factor The determination method is:

[0023]

[0024] k1 / α1 and k2 / α2 represent the effective density of traffic flows 1 and 2 when the space ratios used by traffic flows 1 and 2 are α1 and α2 respectively. (k1 / α1) / (k2 / α2) is the number of traffic flows 2 sensed by traffic flow 1 converted into traffic flow 1, and (k1 / α1) / (k3 / α3) is the number of traffic flows 3 sensed by traffic flow 1 converted into traffic flow 1. When there is only one type of traffic flow, k p = k;

[0025] The expressions of space allocation and perceived density under the different states are established as follows:

[0026] In free flow state:

[0027] When the mixed bicycle traffic flow is in a free flow state, the three types of vehicles all travel on the road segment at a free speed, the interaction between vehicles is small, and the space distribution on the road segment is:

[0028]

[0029] The perceived density is:

[0030]

[0031] In semi-congested state:

[0032] When the mixed bicycle traffic flow is in a semi-congested state, at least one or two traffic flows are in a congested state. Taking traffic flow 1 as free and traffic flows 2 and 3 as congested as an example, the space allocation on the road section is:

[0033]

[0034] The perceived density is:

[0035]

[0036] In full congestion:

[0037] When the mixed bicycle traffic flow is in a completely congested state, the three types of traffic have no space to maintain the free flow speed. The three types of traffic can be considered to have the same speed. Therefore, the road space can be allocated to the three types of traffic to make their speeds the same. Their speeds can be determined by the traffic basic diagram of each traffic flow. The space allocation on the road section is:

[0038] α1+α2+α3=1

[0039]

[0040] The perceived density is:

[0041]

[0042] Among them, α1, α2 and α3 represent the coefficients of the physical space that traffic flow 1, traffic flow 2 and traffic flow 3 can move, respectively; k1, k2 and k3, and and and They represent the density, critical density, perceived spatial density and blocking density of traffic flow 1, traffic flow 2 and traffic flow 3 respectively; w1, w2 and w3 represent the departure wave speeds of traffic flow 1, traffic flow 2 and traffic flow 3 respectively.

[0043] Furthermore, the S2 at least includes the following steps:

[0044] Assume that slow bicycles drive on the right, and faster electric bicycles drive on the left. In addition, the virtual lanes can be divided differently in the three states of mixed bicycle traffic flow:

[0045] When all traffic flows in the road section cell are in a free flow state, and different types of traffic flows do not interfere with each other and run in specific intervals, it can be assumed that there are three virtual lanes on the road section, and a mixed bicycle team evolution model is constructed according to homogeneous traffic flow in each lane;

[0046] When the traffic flow in the road cell is in a semi-congested state, it can be assumed that there are two virtual lanes on the road. The lane in the congested state describes the heterogeneity of the mixed bicycle flow according to the spatial ratio perception density;

[0047] When the traffic flow in the road cell is completely congested, various types of bicycle flows are mixed together, and the vehicles interfere with each other strongly. There are no virtual lanes on the road, and the heterogeneity of mixed bicycle traffic flow is described according to the spatial ratio and perceived density.

[0048] Constructing a model of the evolution of a mixed cycling team:

[0049] Three pseudo lanes are set in the free flow state, which are three cells;

[0050] In the semi-congested state, two pseudo lanes are set according to the perceived density, which is a double cell;

[0051] The fully congested state is when there is no pseudo lane, and the heterogeneity of mixed bicycle traffic flow is described by spatial ratio and perceived density, which is a unit cell;

[0052] Cell division is performed based on the number of pseudo lanes assumed to exist;

[0053] The unit cell is i; the double cells are i, 2 and i, 1; the triple cells are i, 3, i, 2 and i, 1;

[0054] Set the maximum flow of traffic 1, traffic 2 and traffic 3 to: and

[0055] The critical density of traffic flow 1, traffic flow 2 and traffic flow 3 is set as: and

[0056] The blocking density of traffic flow 1, traffic flow 2 and traffic flow 3 is set as: and

[0057] Set the starting wave speed of traffic flow 1, traffic flow 2 and traffic flow 3 to and

[0058] The departure wave speeds of traffic flow 1, traffic flow 2 and traffic flow 3 are set to w1, w2 and w3.

[0059] Furthermore, the S3 at least includes the following steps:

[0060] Since three types of cells are divided according to different types of bicycle states on the road section, the transmission between different cells can be divided into 9 types of transmission. The transmission flow between different cells is calculated separately to better describe the transmission process between cells:

[0061] Schematic diagram of unit cell to unit cell transmission. When the road section is a single lane, the cross-sectional flow rate during unit cell to unit cell transmission is:

[0062] q i (t)Δt=min{S i-1 (t),R i (t)}

[0063] From unit cell to double cell, for the flow transmission from unit cell to double cell, take section i-1 as a fully congested state and section i as a semi-congested state as an example. All three traffic flows exist, traffic flow 1 is a free flow, and traffic flows 2 and 3 are congested flows. Then the sending function S of section i-1 is i-1 (t), the receiver functions of lane i on road section are

[0064] Expected flow rate T from road section i-1 into lane l of road section i i,l (t):

[0065] T i,l (t)·Δt=S i-1 (t)

[0066]

[0067] q i,l (t) = Y l ·T i,l (t)

[0068] Therefore, the flow rate of the section between section i-1 and section i during the time from t to t+Δt is:

[0069]

[0070] From unit cell to three-cell, for the traffic transmission from unit cell to three-cell, the sending function S of the i-1 segment is i-1 (t), the receiver functions of lane i on road section are The flow rate from section i-1 to lane l of section i is the same as the expected flow rate T from the unit cell to the double cell i,l (t) is consistent, so the flow rate of the section between section i-1 and section i during the time from t to t+Δt is:

[0071]

[0072] For the traffic transmission from double cell to single cell, the sending function S of the i-1 segment is i-1，i (t)(l=1,2), the receiver function R of lane l of road section i i (t);

[0073] Since all vehicles on lane 1 of section i-1 enter section i, calculate the flow rate of lane 1 of section i-1 entering section i

[0074]

[0075] q i，1 (t) = Y1·S i-1，1 (t)

[0076] Similarly, we can get q i，2 (t), so the flow rate of the section between section i-1 and section i during the time from t to t+Δt is:

[0077]

[0078] From double cell to double cell, lane 2 is a mixture of traffic flow 2 and traffic flow 3. Since the lanes of the two cellular sections do not affect each other, the following is true:

[0079] S i-1，l (t) = min{k i-1，l (t)Δx,q m Δt}

[0080]

[0081] q i,l (t) = min{S i-1，l (t), R i，l (t)}

[0082]

[0083] From double cells to triple cells, and from double cells to triple cells, the sending function S of the i-1 segment is i-1，l (t)(l=1,2), the receiver function R of lane l of road section i i，l′ (t)(l′=1,2,3);

[0084] Assuming that the vehicle in lane 1 only travels in lane 1, it can be regarded as a combination of unit cell to double cell transmission and unit cell to unit cell transmission, which can be obtained as follows:

[0085]

[0086] q i，l (t) = Y l ·S i-1，l (t), (l = 2, 3)

[0087] q i，1 (t)·Δt=min{S i-1，1 (t), R i，1 (t)}

[0088]

[0089] From three cells to a single cell, the definition of the sending function and receiving function is summarized by the CTM model. The sending function S of lane 1 of section i-1 is i-1，1 (t), the sending function S of lane 2 i-1，2 (t), the sending function S of lane 3 i-1，3 (t), the receiver function R of road segment i i，l (t), since all vehicles on the lane of section i-1 enter section i, the flow rate of lane 2 of section i-1 entering section i is calculated, and the following is obtained:

[0090]

[0091] q i，2 (t) = Y2·S i-1，2 (t)

[0092] Therefore, the flow rate of the section between section i-1 and section i during the time from t to t+Δt is:

[0093]

[0094] From three cells to two cells, and from three cells to two cells, the sending function S of the i-1 segment is i-1，l (t)(l=1,2,3), the receiver function R of lane l in section i i，l′ (t)(l′=1,2), we can get the following:

[0095]

[0096] q i，l (t) = Y l ·S i-1，l (t), (l=2,3)

[0097]

[0098] From three cells to three cells, since the lanes in the three-cell road section do not affect each other, we can get the following:

[0099] S i-1，l (t) = min{k i-1，l (t)Δx,q m Δt}

[0100]

[0101] q i,l (t)Δt=min{S i-1，l (t), R i，l (t)}

[0102] The flow rate of the three-cell to three-cell transmission section is:

[0103]

[0104] Compared with the prior art, the present invention has the following beneficial effects:

[0105] (1) The model of the present invention takes into account the heterogeneity of mixed bicycle traffic flow, while the Robertson model and the CTM model only roughly consider the mixed bicycle traffic flow as homogeneous traffic flow. The prediction results of the model of the present invention are closer to the actual data.

[0106] (2) The mean square error of the model of the present invention relative to the homogeneous traffic flow model was improved in both time periods of the three road sections. Compared with the Robertson model, the mean square error of the model of the present invention was improved by an average of 32.38%. Compared with the CTM, the mean square error of the model of the present invention was improved by an average of 28.88%. This shows that the model of the present invention can better reflect the characteristics of heterogeneous mixed bicycle traffic flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0108] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0109] Figure 2 It is a schematic diagram of the space ratio between the ordinary bicycle traffic flow and the electric bicycle traffic flow of the present invention;

[0110] Figure 3 It is a schematic diagram of the process of dividing the road section into cells according to the present invention;

[0111] Figure 4 It is a schematic diagram of the cell division of the road section of the present invention;

[0112] Figure 5 is a basic diagram of bicycle traffic flow of the present invention;

[0113] Figure 6 It is a schematic diagram of the cell transmission of the present invention;

[0114] Figure 7 It is the distribution diagram of the upstream intersection departure flow rate of the present invention;

[0115] Figure 8 A comparison diagram of the flow rate at the downstream intersection of the present invention;

[0116] Fig. 9 This is a comparison chart of mean square errors of different models of the present invention. DETAILED DESCRIPTION

[0117] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0118] Embodiment 1:

[0119] See also Figure 1 The present invention is used to disclose a method for estimating the evolution state of a heterogeneous bicycle team based on perception density and pseudo lane theory. Specifically, the steps are as follows:

[0120] Step 1: Based on the perceived density, the heterogeneity of mixed bicycle traffic flow is described, bicycle traffic types are divided, and the spatial allocation and perceived density expressions under three mixed bicycle traffic flow states are obtained. The spatial ratio and perceived density are used to explain the segmentation of non-motorized vehicle roads.

[0121] Specifically, this step includes two steps, as follows:

[0122] Step 1.1: The ordinary bicycle traffic flow in the non-motorized vehicle lane is traffic flow 1, the bicycle-type electric bicycle traffic flow is traffic flow 2, and the scooter-type electric bicycle traffic flow is traffic flow 3. The densities are k1, k2, and k3, respectively. Figure 2As shown, α1, α2 and α3 represent the coefficients (physical space ratios) of the physical space in which traffic flow 1, traffic flow 2 and traffic flow 3 can move, respectively, and α1+α2+α3≤1.

[0123] The mutual influence between different types of mixed bicycles is characterized by density, and the perceived density is used to explain the road segmentation. The other types of vehicles around traffic flow 1 are regarded as traffic flow 1 with converted density.

[0124]

[0125] Taking traffic flow 1 as an example, if there is no traffic flow 1, its equivalent density and spatial proportion are zero. When traffic flow 1 exists, the spatial density perceived by traffic flow 1 is The perceived density of traffic flow 2 converted to traffic flow 1 is The perceived density of traffic flow 3 converted to traffic flow 1 is in Depends on the space conversion factor, The physical meaning is that traffic flow 2 and traffic flow 3 have the same traffic conditions as traffic flow 1 (i.e., the density of other types of traffic flow and the density of traffic flow 1), and traffic flow 1 converts traffic flow 2 into the same type of traffic flow, and traffic flow 1 converts traffic flow 3 into the same type of traffic flow, and occupies a certain part of the total space to affect the movement of traffic flow 1. Conversion coefficient Determine the method

[0126]

[0127] k1 / α1 and k2 / α2 represent the effective density of traffic flows 1 and 2 when the space ratios used by traffic flows 1 and 2 are α1 and α2, respectively. (k1 / α1) / (k2 / α2) is the number of traffic flows 2 sensed by traffic flow 1 converted into traffic flow 1, and (k1 / α1) / (k3 / α3) is the number of traffic flows 3 sensed by traffic flow 1 converted into traffic flow 1. When there is only one type of traffic flow, k p =k.

[0128] Step 1.2: Establish the spatial allocation and perceived density expressions under the different states:

[0129] (1) Free flow state

[0130] When the mixed bicycle traffic flow is in a free flow state, the three types of vehicles all travel on the road segment at a free speed, the interaction between vehicles is small, and the space distribution on the road segment is:

[0131]

[0132] The perceived density is:

[0133]

[0134] (2) Semi-congested state

[0135] When the mixed bicycle traffic flow is in a semi-congested state, at least one or two traffic flows are in a congested state. Taking traffic flow 1 as free and traffic flows 2 and 3 as congested as an example, the space allocation on the road section is:

[0136]

[0137] The perceived density is:

[0138]

[0139] (3) Complete congestion

[0140] When the mixed bicycle traffic flow is in a completely congested state, the three types of traffic have no space to maintain the free flow speed. The three types of traffic can be considered to have the same speed. Therefore, the road space can be allocated to the three types of traffic to make their speeds the same. Their speeds can be determined by the traffic basic diagram of each traffic flow. The space allocation on the road section is:

[0141] α1+α2+α3=1

[0142]

[0143] The perceived density is:

[0144]

[0145] Among them, α1, α2 and α3 represent the coefficients of the physical space that traffic flow 1, traffic flow 2 and traffic flow 3 can move, respectively; k1, k2 and k3, and and and They represent the density, critical density, perceived spatial density and blocking density of traffic flow 1, traffic flow 2 and traffic flow 3 respectively; w1, w2 and w3 represent the departure wave speeds of traffic flow 1, traffic flow 2 and traffic flow 3 respectively.

[0146] Step 2: Based on the pseudo lane hypothesis cell division, the three states of spatial ratio and perceived density are described more intuitively;

[0147] Specifically, this step includes two steps as follows:

[0148] Step 2.1: Assume that slow bicycles drive on the right, and faster electric bicycles drive on the left. The virtual lanes can be divided differently in the three states of mixed bicycle traffic flow:

[0149] When all traffic flows in a road section cell are in a free flow state, and different types of traffic flows do not interfere with each other and run in specific intervals, it can be assumed that there are three virtual lanes on the road section, and a mixed bicycle team evolution model is constructed according to homogeneous traffic flow in each lane.

[0150] When the traffic flow in the road cell is in a semi-congested state, it can be assumed that there are two virtual lanes on the road section. The lane in the congested state describes the heterogeneity of the mixed bicycle flow based on the spatial ratio perception density.

[0151] When the traffic flow in the road cell is completely congested, various types of bicycle flows are mixed together, the vehicles interfere with each other strongly, and there are no virtual lanes on the road. The heterogeneity of mixed bicycle traffic flow is described according to the spatial ratio and perceived density.

[0152] Step 2.2: Construct a hybrid cycling team evolution model. The model construction process is as follows: Figure 3 As shown in , the cell division is performed according to the number of pseudo lanes assumed to exist, as Figure 4 The basic diagram of bicycle traffic flow is shown in Figure 5 shown.

[0153] in, and and and are the maximum flow, critical density and blocking density of traffic flow 1, traffic flow 2 and traffic flow 3 respectively, and are the starting wave velocities of traffic flow 1, traffic flow 2 and traffic flow 3 respectively, and w1, w2 and w3 are the leaving wave velocities of traffic flow 1, traffic flow 2 and traffic flow 3 respectively.

[0154] Step 3: Based on the perception density expression and combined with the pseudo lane hypothesis theory, a model for estimating the evolution state of a heterogeneous bicycle team is established;

[0155] In this step, since three types of cells are divided according to different types of bicycle states on the road section, the transmission between different cells can be divided into 9 types of transmission. The transmission diagram is shown in the figure. Figure 6 As shown. The transmission flow between different cells is calculated separately to better describe the transmission process between cells: Case 1 unit cell to unit cell:

[0156] like Figure 7 (a) Schematic diagram of unit cell to unit cell transmission. When the road section is a single lane, the cross-sectional flow rate during unit cell to unit cell transmission is:

[0157] q i (t)Δt=min{S i-1 (t),R i (t)}

[0158] Case 2. Single cell to double cell

[0159] For the flow transmission from a single cell to a double cell, take the case where the section i-1 is in a fully congested state and the section i is in a semi-congested state. All three traffic flows exist, traffic flow 1 is a free flow, and traffic flows 2 and 3 are congested flows. Then the sending function S of the section i-1 is i-1 (t), the receiver functions of lane i on road section are

[0160] Expected flow rate T from road section i-1 into lane l of road section i i,l (t):

[0161] T i,l (t)·Δt=S i-1 (t)

[0162]

[0163] q i,l (t) = Y l ·T i,l (t)

[0164] Therefore, the flow rate of the section between section i-1 and section i during the time from t to t+Δt is:

[0165]

[0166] Case 3. Single cell to triple cell

[0167] For the traffic transmission from a single cell to a triple cell, the sending function S of the i-1 segment is i-1 (t), the receiver functions of lane i on road section are The flow rate from section i-1 to lane l of section i is the same as the expected flow rate T from the unit cell to the double cell i,l (t) is consistent, so the flow rate of the section between section i-1 and section i during the time from t to t+Δt is:

[0168]

[0169] Case 4. Double Cell to Single Cell

[0170] For the traffic transmission from double cells to single cells, the sending function S of the i-1 segment is i-1,l (t)(l=1,2), the receiver function R of lane l of road section i i (t).

[0171] Since all vehicles on lane 1 of section i-1 enter section i, calculate the flow rate of lane 1 of section i-1 entering section i

[0172]

[0173] q i,1 (t) = Y1·S i-1,1 (t)

[0174] Similarly, we can get q i,2 (t), so the flow rate of the section between section i-1 and section i during the time from t to t+Δt is:

[0175]

[0176] Case 5. Two-cell to two-cell

[0177] Lane 2 is a mixed traffic of traffic 2 and traffic 3. Since the two cell road section lanes do not affect each other,

[0178] S i-1,l (t) = min{k i-1,l (t)Δx,q m Δt}

[0179]

[0180] q i，l (t) = min{S i-1,l (t), R i，l (t)}

[0181]

[0182] Case 6. From two cells to three cells

[0183] From double cells to triple cells, the sending function S of the i-1 segment is i-1，l (t)(l=1,2), the receiver function R of lane l of road section i i，l′ (t)(l′=1,2,3).

[0184] Assuming that the vehicle in lane 1 only travels in lane 1, it can be regarded as a combination of unit cell to two-cell transmission and unit cell to unit cell transmission.

[0185]

[0186] q i，l (t) = Y l ·S i-1,l (t), (l = 2, 3)

[0187] q i，1 (t)·Δt=min{S i-1，1 (t), R i，1 (t)}

[0188]

[0189] Case 7. From triple cell to single cell

[0190] The definition of the sending function and receiving function is summarized by the CTM model. The sending function S of lane 1 of section i-1 is i-1，1 (t), the sending function S of lane 2 i-1，2 (t), the sending function S of lane 3 i-1，3 (t), the receiver function R of road segment i i，l (t), since all vehicles on lane 2 of section i-1 enter section i, calculate the flow rate of lane 2 of section i-1 entering section i,

[0191]

[0192] q i，2 (t) = Y2·S i-1，2 (t)

[0193] Therefore, the flow rate of the section between section i-1 and section i during the time from t to t+Δt is:

[0194]

[0195] Case 8. From three cells to two cells

[0196] From three cells to two cells, the sending function S of the i-1 segment is i-1,l (t)(l=1,2,3), the receiver function R of lane l in section i i,l′ (t)(l′=1,2)

[0197]

[0198] q i，l (t) = Y l ·S i-1,l (t), (l=2,3)

[0199]

[0200] Case 9. Triple Cell to Triple Cell

[0201] Since the lanes of the three-cell road section do not affect each other,

[0202] S i-1,l (t) = min{k i-1,l (t)Δx,q m Δt}

[0203]

[0204] qi，l (t)Δt=min{S i-1,l (t), R i，l (t)}

[0205] The flow rate of the three-cell to three-cell transmission section is:

[0206]

[0207] Step 4: Analyze the departure flow at the upstream intersection and the arrival flow at the downstream intersection through data collection analysis and parameter estimation;

[0208] In this step, three sections of roads in Kunming, China, namely Baita Road, Dongfeng East Road and North Ring Road, were investigated. The data collection time was two hours each during off-peak and peak hours, from 15:30 to 19:30. The separation between vehicles and non-vehicles on Baita Road and Dongfeng East Road was mainly fenced, while the separation between vehicles and non-vehicles on North Ring Road had two facilities, fenced and green belts, which could eliminate the impact of motor vehicles on mixed bicycle traffic flow. The collected data included four basic information on the upstream intersection cycle, vehicle and non-vehicle separation method, and road section width and length.

[0209] Taking the selected Baita Road as an example, the road section is 500m long and the non-motorized vehicle lane is 3m wide. According to the data, the average vehicle speed is 33km / h and the interval is 5s. The road section can be divided into 10 cells on average, each cell is 50m long, and three flow observation sections are set, namely A, B, and C. Section A is the section where vehicles are released from the upstream intersection, section B is the first cell flow output section, the distance between A and B is 50m, and section C is the downstream arrival section of the road section.

[0210] According to the actual survey data, considering the heterogeneity of bicycle traffic flow, different types of bicycle traffic flow may be in different traffic states on the same road section. It is necessary to determine the values ​​of the parameters of the CTM model for different types of bicycle traffic flow. The critical density of ordinary bicycle flow is The crowding density is The maximum flow rate is The critical density of a bicycle-type electric bicycle is The crowding density is The maximum flow rate is The critical density of a bicycle-type electric bicycle is The crowding density is The maximum flow rate is

[0211] Analyze the departing flow at the upstream intersection and the arriving flow at the downstream intersection;

[0212] First, through field investigation and collected data, it can be known that the survey data can be statistically analyzed at a time interval of 5s to obtain the average vehicle departure and arrival flow rate distribution within the upstream intersection and downstream section cycle. The average departure flow rate distribution of the three types of non-motor vehicles at the upstream intersection is as follows: Figure 7 Among them, HB is a hybrid bicycle, RB is a normal bicycle, B-EB is a bicycle-type electric bicycle, and S-EB is a pedal-type electric bicycle.

[0213] Depend on Figure 7 It can be seen that pedal-type electric bicycles have the largest flow rate and ordinary bicycles have the smallest flow rate. In urban road non-motorized vehicle traffic, pedal-type electric bicycles account for the largest proportion of the total number of bicycles, and ordinary bicycles account for the smallest proportion.

[0214] The available models for predicting the mixed bicycle traffic flow at the downstream intersection include two types of homogeneous traffic flow models: the classic fleet discrete model and the classic cellular transmission model. The prediction results of the mixed bicycle traffic flow by these two types of homogeneous traffic flow models and the heterogeneous bicycle traffic flow model established in the present invention are as follows: Figure 8 shown.

[0215] Step 5: Compare the constructed model with the classic fleet discrete model Robertson model and the traditional CTM model to evaluate the advantages of the model of the present invention.

[0216] In order to quantitatively describe the prediction effects of the model of the present invention, the classic fleet discrete model Robertson model and the traditional CTM model, the mean square error values ​​of the predicted and actual vehicle arrival flow rate values ​​using the three models can be calculated and statistically analyzed, as shown in Table 3, and a comparison chart is drawn, as shown in Table 3. Fig. 9 shown.

[0217] Table 3 Comparison of mean square error of different models

[0218]

[0219] Note: The conversion coefficient of bicycles and electric vehicles in CTM is 1.2295; the error improvement ratio is the percentage of mean square error improvement of the model of the present invention relative to other models.

[0220] To quantitatively describe the prediction effect of PPCTM, the classic Robertson model and the traditional CTM, we can calculate Figure 8 The mean square error between the predictions of different models and the actual vehicle arrival flow values ​​of the three mixed bicycle traffic flows in the two periods of off-peak and peak hours is plotted, and a comparison chart is drawn as follows: Fig. 9 shown.

[0221] (1) As can be seen from Table 3, since the model of the present invention takes into account the heterogeneity of mixed bicycle traffic flow, while the Robertson model and CTM model only roughly consider the mixed bicycle traffic flow as homogeneous traffic flow, the prediction results of the model of the present invention are closer to the actual data.

[0222] (2) From the analysis in Table 3, it can be seen that the mean square error of the model of the present invention relative to the homogeneous traffic flow model has been improved in both time periods of the three sections. Compared with the Robertson model, the mean square error of the model of the present invention has been improved by an average of 32.38%. Compared with the CTM, the mean square error of the model of the present invention has been improved by an average of 28.88%, indicating that the model of the present invention can better reflect the characteristics of the heterogeneous traffic flow of mixed bicycles.

[0223] From the above, we can know that:

[0224] The present invention is based on the theory of perceived density and pseudo-lane to characterize the evolution state of heterogeneous bicycle fleets. First, the operating characteristics of heterogeneous bicycle traffic flow on urban roads are deeply analyzed, and the theory is integrated into the traffic flow cellular transmission model framework to establish a perceived density and pseudo-lane cellular transmission model that takes into account the characteristics of heterogeneous bicycle traffic flow; then, through model verification, the established model is scientifically compared and evaluated with the classic Robertson discrete model and CTM model. The model of the present invention can fully describe the evolution law of mixed bicycle fleets under heterogeneous traffic flow conditions, and has important guiding significance for lane planning and signal timing optimization in mixed bicycle environments.

[0225] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for estimating the evolutionary state of a heterogeneous bicycle team, characterized by: At least the following steps are included: S1: Based on the perceived density, the heterogeneity of mixed bicycle traffic flow is described, bicycle traffic types are divided, the spatial allocation and perceived density expressions under three mixed bicycle traffic flow states are obtained, and the spatial ratio and perceived density are used to explain the segmentation of non-motorized vehicle roads; S2: Based on the pseudo lane hypothesis cell division, the three states of spatial ratio and perceived density are described more intuitively; S3: Based on the perception density expression and combined with the pseudo lane hypothesis theory, a model for estimating the evolution state of a heterogeneous bicycle team is established. S4: Through data collection and analysis and parameter estimation, the outgoing flow at the upstream intersection and the arriving flow at the downstream intersection are analyzed, and then the evolution state of the heterogeneous bicycle team is estimated to obtain the prediction result of the mixed bicycle traffic flow; S5: The constructed model is compared with the classic fleet discrete model Robertson model and the traditional CTM model to evaluate the advantages of the model of the present invention.

2. The method for estimating the evolutionary state of a heterogeneous bicycle team according to claim 1, characterized in that: The S1 at least comprises the following steps: The ordinary bicycle traffic flow in the non-motorized vehicle lane is set as traffic flow 1, the bicycle-type electric bicycle traffic flow is set as traffic flow 2, and the scooter-type electric bicycle traffic flow is set as traffic flow 3; the densities are k1, k2, and k3 respectively, and α1, α2, and α3 are used to represent the coefficients of the movable physical space of traffic flow 1, traffic flow 2, and traffic flow 3, respectively, and α1+α2+α3≤1; The mutual influence between different types of mixed bicycles is characterized by density, and the perceived density is used to explain the road segmentation. The other types of vehicles around traffic flow 1 are regarded as traffic flow 1 with converted density, and the following formula is obtained: Taking traffic flow 1 as an example, if there is no traffic flow 1, its equivalent density and spatial share are zero; When there is traffic flow 1, the spatial density perceived by traffic flow 1 is The perceived density of traffic flow 2 converted to traffic flow 1 is The perceived density of traffic flow 3 converted to traffic flow 1 is in Depends on the space conversion factor, The physical meaning of is that the traffic conditions of traffic flow 2 and traffic flow 3 are the same as those of traffic flow 1, that is, the density of other types of traffic flows and the density of traffic flow 1; Traffic flow 1 converts all traffic flow 2 into the same type of traffic flow, and traffic flow 1 converts all traffic flow 3 into the same type of traffic flow, and occupies a certain part of the total space to affect the movement of traffic flow 1; Conversion Factor The determination method is: k1 / α1 and k2 / α2 represent the effective density of traffic flows 1 and 2 when the space ratios used by traffic flows 1 and 2 are α1 and α2 respectively. (k1 / α1) / (k2 / α2) is the number of traffic flows 2 sensed by traffic flow 1 converted into traffic flow 1, and (k1 / α1) / (k3 / α3) is the number of traffic flows 3 sensed by traffic flow 1 converted into traffic flow 1. When there is only one type of traffic flow, k p = k; The expressions of space allocation and perceived density under the different states are established as follows: In free flow state: When the mixed bicycle traffic flow is in a free flow state, the three types of vehicles all travel on the road segment at a free speed, the interaction between vehicles is small, and the space distribution on the road segment is: The perceived density is: In semi-congested state: When the mixed bicycle traffic flow is in a semi-congested state, at least one or two traffic flows are in a congested state. Taking traffic flow 1 as free and traffic flows 2 and 3 as congested as an example, the space allocation on the road section is: The perceived density is: In full congestion: When the mixed bicycle traffic flow is in a completely congested state, the three types of traffic have no space to maintain the free flow speed. The three types of traffic can be considered to have the same speed. Therefore, the road space can be allocated to the three types of traffic to make their speeds the same. Their speeds can be determined by the traffic basic diagram of each traffic flow. The space allocation on the road section is: α1+α2+α3＝1 The perceived density is: Among them, α1, α2 and α3 represent the coefficients of the physical space that traffic flow 1, traffic flow 2 and traffic flow 3 can move, respectively; k1, k2 and k3, and and and They represent the density, critical density, perceived spatial density and blocking density of traffic flow 1, traffic flow 2 and traffic flow 3 respectively; w1, w2 and w3 represent the departure wave speeds of traffic flow 1, traffic flow 2 and traffic flow 3 respectively.

3. The method for estimating the evolution state of a heterogeneous bicycle team according to claim 2, characterized in that: The S2 at least comprises the following steps: Assume that slow bicycles drive on the right, and faster electric bicycles drive on the left. In addition, the virtual lanes can be divided differently in the three states of mixed bicycle traffic flow: When all traffic flows in the road section cell are in a free flow state, and different types of traffic flows do not interfere with each other and run in specific intervals, it can be assumed that there are three virtual lanes on the road section, and a mixed bicycle team evolution model is constructed according to homogeneous traffic flow in each lane; When the traffic flow in the road cell is in a semi-congested state, it can be assumed that there are two virtual lanes on the road. The lane in the congested state describes the heterogeneity of the mixed bicycle flow according to the spatial ratio perception density; When the traffic flow in the road cell is completely congested, various types of bicycle flows are mixed together, and the vehicles interfere with each other strongly. There are no virtual lanes on the road, and the heterogeneity of mixed bicycle traffic flow is described according to the spatial ratio and perceived density. Constructing a model of the evolution of a mixed cycling team: Three pseudo lanes are set in the free flow state, which are three cells; In the semi-congested state, two pseudo lanes are set according to the perceived density, which is a double cell; The fully congested state is when there is no pseudo lane, and the heterogeneity of mixed bicycle traffic flow is described by spatial ratio and perceived density, which is a unit cell; Cell division is performed based on the number of pseudo lanes assumed to exist; The unit cell is i; the double cells are i, 2 and i, 1; the triple cells are i, 3, i, 2 and i, 1; Set the maximum flow of traffic 1, traffic 2 and traffic 3 to: and The critical density of traffic flow 1, traffic flow 2 and traffic flow 3 is set as: and The blocking density of traffic flow 1, traffic flow 2 and traffic flow 3 is set as: and Set the starting wave speed of traffic flow 1, traffic flow 2 and traffic flow 3 to and The departure wave speeds of traffic flow 1, traffic flow 2 and traffic flow 3 are set to w1, w2 and w3.

4. The method for estimating the evolutionary state of a heterogeneous bicycle team according to claim 3, characterized in that: The S3 at least includes the following steps: Since three types of cells are divided according to different types of bicycle states on the road section, the transmission between different cells can be divided into 9 types of transmission. The transmission flow between different cells is calculated separately to better describe the transmission process between cells: Schematic diagram of unit cell to unit cell transmission. When the road section is a single lane, the cross-sectional flow rate during unit cell to unit cell transmission is: q i (t)Δt=min{S i-1 (t),R i (t)} From unit cell to double cell, for the flow transmission from unit cell to double cell, take section i-1 as a fully congested state and section i as a semi-congested state as an example. All three traffic flows exist, traffic flow 1 is a free flow, and traffic flows 2 and 3 are congested flows. Then the sending function S of section i-1 is i-1 (t), the receiver functions of lane i on road section are Expected flow rate T from road section i-1 into lane l of road section i i,l (t): T i,l (t)·Δt=S i-1 (t) q i,l (t)=Y l ·T i,l (t) Therefore, the flow rate of the section between section i-1 and section i during the time from t to t+Δt is: From unit cell to three-cell, for the traffic transmission from unit cell to three-cell, the sending function S of the i-1 segment is i-1 (t), the receiver functions of lane i on road section are The flow rate from section i-1 to lane l of section i is the same as the expected flow rate T from the unit cell to the double cell i,l (t) is consistent, so the flow rate of the section between section i-1 and section i during the time from t to t+Δt is: For the traffic transmission from double cell to single cell, the sending function S of the i-1 segment is i-1,l (t)(l=1,2), the receiver function R of lane l of road section i i (t); Since all vehicles on lane 1 of section i-1 enter section i, calculate the flow rate of lane 1 of section i-1 entering section i q i,1 (t)=Y1·S i-1,1 (t) Similarly, we can get q i,2 (t), so the flow rate of the section between section i-1 and section i during the time from t to t+Δt is: From double cell to double cell, lane 2 is a mixture of traffic flow 2 and traffic flow 3. Since the lanes of the two cellular sections do not affect each other, the following is true: S i-1,l (t)=min{k i-1,l (t)Δx,q m Δt} q i,l (t)=min{S i-1,l (t),R i,l (t)} From double cells to triple cells, and from double cells to triple cells, the sending function S of the i-1 segment is i-1,l (t)(l=1,2), the receiver function R of lane l of road section i i,l' (t)(l'=1,2,3); Assuming that the vehicle in lane 1 only travels in lane 1, it can be regarded as a combination of unit cell to double cell transmission and unit cell to unit cell transmission, which can be obtained as follows: q i,l (t)=Y l ·S i-1,l (t),(l=2,3) q i,1 (t)·Δt=min{S i-1,1 (t),R i,1 (t)} From three cells to a single cell, the definition of the sending function and receiving function is summarized by the CTM model. The sending function S of lane 1 of section i-1 is i-1,1 (t), the sending function S of lane 2 i-1,2 (t), the sending function S of lane 3 i-1,3 (t), the receiver function R of road segment i i,l (t), since all vehicles on the lane of section i-1 enter section i, the flow rate of lane 2 of section i-1 entering section i is calculated, and the following is obtained: q i,2 (t)=Y2·S i-1,2 (t) Therefore, the flow rate of the section between section i-1 and section i during the time from t to t+Δt is: From three cells to two cells, and from three cells to two cells, the sending function S of the i-1 segment is i-1,l (t)(l=1,2,3), the receiver function R of lane l in section i i,l' (t)(l'=1,2), we can get the following: q i,l (t)=Y l ·S i-1,l (t),(l=2,3) From three cells to three cells, since the lanes in the three-cell road section do not affect each other, we can get the following: S i-1,l (t)=min{k i-1,l (t)Δx,q m Δt} q i,l (t)Δt=min{S i-1,l (t),R i,l (t)} The flow rate of the three-cell to three-cell transmission section is:

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