Urban branch network one-way traffic organization optimization method

Abstract

The invention discloses an urban branch network one-way traffic organization optimization method, and relates to the technical field of traffic planning. Comprising the following steps: investigating resident travel behaviors to obtain resident travel demand data; urban road network parameters are investigated, attribute parameter values of an urban branch network are obtained, and a road section adjacency relation matrix and a road section-intersection steering connection relation matrix are constructed; constructing an urban branch network one-way traffic organization optimization model, and calibrating and solving parameters; and analyzing traffic management requirements of the branch network, and selecting a one-way traffic organization optimization scheme of the branch network according to requirements. According to the method, based on a network balance flow distribution idea, the safety performance and the traffic efficiency are taken as main factors of branch network design, the one-way traffic organization optimization model is constructed, the one-way traffic organization scheme with safety and efficiency preference characteristics can be generated while the overall safety performance and the traffic efficiency of a road network are balanced, and the method is suitable for popularization and application. And the defect that the traffic safety of travelers is considered in the road network design of a traditional traffic organization scheme is overcome.

Description

Technical Field

[0001] The invention relates to the technical field of traffic planning, and in particular to a method for optimizing one-way traffic organization of an urban road network. Background Art

[0002] With the introduction of the concepts of "sustainable" traffic safety, the vision of "zero fatalities" in traffic, and the construction of an "intrinsically safe" traffic system, the traffic network design theory for active safety control has emerged, requiring that the safety and traffic efficiency of the road network should be regarded as equally important optimization goals during the design stage. Therefore, it is an urgent technical problem to propose a method that can reasonably allocate the traffic flow of the road network and balance the safety performance and traffic efficiency requirements of the road network based on the known travel needs of residents and the road network attribute parameters, and implement one-way traffic organization optimization for the urban branch road network.

[0003] The optimization of one-way traffic organization in branch road networks is an important part of urban traffic network design and a core technology for improving road network safety and traffic efficiency. Urban traffic network design refers to: under the existing investment scale, by improving or building new road facilities in the existing network (improving or proposing new network traffic management solutions), the target system performance of the entire traffic network is optimized. However, most traditional methods use single or comprehensive system indicators such as reducing impedance, reducing construction / reconstruction costs, reducing energy consumption, and improving travel fairness as optimization targets. Few methods consider the trade-off between road network safety and efficiency based on balanced flow when designing a road network. Limited by traditional traffic network design solutions that take travel efficiency, fairness, etc. as single optimization targets, existing road network optimization methods cannot maximize traffic safety while ensuring the travel efficiency of travelers. Summary of the invention

[0004] The present invention is based on the idea of ​​network balanced flow distribution to maximize the road network safety performance (minimize the risk of collision) and minimize the road network traffic efficiency, constructs a one-way traffic organization optimization model for urban branch road networks, and based on this model proposes a complete set of one-way traffic organization optimization methods for road network planning considering travelers' safety and efficiency.

[0005] The technical solution adopted by the present invention is: a method for optimizing one-way traffic organization of a city branch road network, comprising the following steps:

[0006] Step 10: Conduct a survey on residents’ travel behavior to obtain residents’ travel demand data;

[0007] Step 20: Survey urban road network parameters, obtain attribute parameter values ​​of urban branch road network and construct road section adjacency relationship matrix and road section-intersection turn connection relationship matrix;

[0008] Step 30: Construction, parameter calibration and solution of one-way traffic organization optimization model of urban branch road network;

[0009] Step 40: Analyze the traffic management requirements of the branch network and select a one-way traffic organization optimization plan for the branch network as needed.

[0010] Preferably, the residents' travel behavior survey in step 10 further includes the following steps:

[0011] Step 101: sampling the survey subjects, wherein the sampling adopts stratified sampling;

[0012] Step 102: Use the RP survey method to conduct a survey on residents’ travel needs, which can be done by home visits to obtain survey data in person. The survey content includes: assuming that there are w types of residents with different travel needs in the area, investigate the travel needs of each type of residents q w , and construct the road network travel OD matrix.

[0013] Preferably, the urban road network parameter survey in step 20 and the construction of the road section adjacency relationship matrix and the road section-intersection turn connection relationship matrix further include the following steps:

[0014] Step 201: Obtain the attribute parameter values ​​of each road section and intersection within the time period T=nt, including the inherent attribute parameter values: traffic capacity c a , zero flow time Length L a , blocking density Free flow speed Variable attribute parameter value: average traffic flow q i (i=1,…,n), average travel time t i (i=1,…,n); Number of road section conflicts k a , intersection conflict times k a→b , Vehicle Quality m , and organize it into a structured data format;

[0015] Step 202: Draw a road network topology diagram According to the inter-cell accessibility relationship shown in the structure diagram, the road section adjacency relationship matrix and the road section-intersection turn connection relationship matrix are constructed, and the road section and intersection attribute parameters are marked. Among them, the node set V = (v1,…,v q ), segment arc A=A O ∪A T ∪A m , is the intersection turning arc set; the road section arc includes: one-way branch road section arc set A O , Bidirectional branch road section set A T and main line segment arc Am .

[0016] Preferably, the construction of the one-way traffic organization optimization model of the urban branch road network in step 30 is specifically a two-layer planning model, further comprising the following steps:

[0017] Step 301: Based on the user balance theory, a branch network balanced traffic assignment model is constructed as a lower layer model, which is as follows:

[0018]

[0019]

[0020] Among them, t a (x) represents the travel time of road section a when the traffic flow is x, f p represents the flow of path p, q w represents the travel demand between OD and w, x a represents the traffic flow of road section a, is the path-segment indication parameter. When segment a is on path p, otherwise

[0021] Step 302: construct an upper model to maximize the road network safety performance (minimize the risk of collision) and minimize the road network traffic efficiency. The objective function is as follows:

[0022]

[0023] minT G (S) = ∑ a∈A t a (x a )×x a

[0024] Among them, R G (S) represents the safety performance value of the road network traffic organization scheme S, T G (S) represents the traffic efficiency of the road network traffic organization plan S;

[0025] The collision risk of a road section is calculated by the following formula:

[0026]

[0027] The intersection collision risk is calculated by the following formula:

[0028]

[0029] B1=b1×k a→b , B2=b2×k a→b

[0030] Among them, b1 and b2 are preset parameters; x a and x a→b Represent the road segments in the road network a The traffic volume at the intersection turning from a to b; t a (x a ) indicates a road segment a The flow rate is x a The passage time of .

[0031] Preferably, the road section collision risk R in step 302 a (x a ) and road network efficiency T G (S), further comprising the following steps:

[0032] Step 3021: Calculate the corresponding collision risk according to the road section type. When road section a is a one-way branch road section or a trunk road, that is, a∈A O or a∈A m , the flow rate of vehicle i is x a The collision risk R caused by following a vehicle a(i) (x a ) and the risk of collision due to adjacent vehicles changing lanes The calculation formula is:

[0033]

[0034] The collision risk parameters include the average collision risk r of vehicle i affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle. a(i) (x a )and Collision risk factor ρ a(i) , average conflict risk threshold λ a(i) (x a )and Risk exposure a(i) (x a )and n a is the number of lanes corresponding to the road section.

[0035] Step 3022: Calculate the collision risk parameter of vehicle i affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle in step 3021, and the calculation formula is:

[0036]

[0037] Among them, D a (x a ) is the flow rate of section a is x a When , the expected average distance between vehicles; l iis the length of vehicle i; r is the driver's reaction time; γ is the speed reduction factor (non-negative value); ω is the vehicle's tire-ground friction coefficient; η is the acceleration due to gravity.

[0038] Step 3023: When section a is a bidirectional branch section, a∈A T , the flow rate of vehicle i is x a The collision risk R caused by following a vehicle a(i) (x a ) and the risk of collision due to adjacent vehicles changing lanes The calculation formula is:

[0039]

[0040]

[0041] in, Indicates that vehicle i passes through a two-way two-lane branch road (n a =2), the risk of collision due to the adjacent vehicle changing lanes; Indicates that vehicle i passes through a two-way multi-lane branch road (n a >2), the risk of collision due to the adjacent vehicle changing lanes.

[0042] Step 3024: Calculate the collision risk parameter of vehicle i in step 3023 affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle, and the calculation formula is:

[0043]

[0044] Step 3025: According to the road section travel time t a (x a ), calculate the sum of the travel time of all travelers in the road network∑ a∈A t a (x a )×x a , which is used to characterize the traffic efficiency of the road network.

[0045] Preferably, the calibration of parameters of the urban branch road network one-way traffic organization optimization model in step 30 further includes the following steps:

[0046] Step 303: Calibrate the blocking coefficient of the BPR function according to the urban branch network attribute parameter value obtained in step 201 α and β, further comprising the following steps:

[0047] Step 3031: construct a parameter fitting function, the calculation formula is:

[0048] Y=β·X+lg(α)

[0049] in,

[0050] Step 3032: The data obtained from step 102 is n Group traffic flow q i and n Group travel time t i The structured data is brought into the fitting function of step 3031 to obtain n A linear equation;

[0051] Step 3033: Based on the result obtained in step 3032 n A linear equation is solved by least squares multivariate linear regression to obtain the retardation coefficient α and β.

[0052] Step 304: Calculate the road segment according to the urban branch road network attribute parameter values ​​obtained in step 201 a When the flow rate is xa, the average expected distance between vehicles is D a (x a ), the calculation formula is:

[0053]

[0054] Where g represents the average distance between randomly distributed vehicles on the road section (m);

[0055] Step 305: According to the resistance coefficient of step 303 α The calibration results of β and β are used to calculate the road section a The flow rate is x a When the average speed of the vehicle is v a , the calculation formula is:

[0056]

[0057] Preferably, the solving of the urban branch road network one-way traffic organization optimization model in step 30 further comprises the following steps:

[0058] Step 306: using a non-dominated classification genetic algorithm to solve the one-way traffic organization optimization model of the urban branch network, obtaining a Pareto front approximation F1 and a better strategy set, further comprising the following steps:

[0059] Step 3061: Input the initial model data, including (the travel demand q obtained in step 101 w , the road section parameters obtained in step 201 c a , L a , and ), and set the model's maximum generation number N, population size M, and crossover probability pc , mutation probability p m , calculate the error limit ε (Set according to accuracy requirements);

[0060] Step 3062: Set the initial generation number n←0 to generate the parent population P n (P n ={S1,S2,…,S n}), the parent population is composed of randomly generated road network traffic organization scheme S k Fill composition, set the offspring population Where S k ={s(a),a∈A O ∪A T}(k=1,…,n), s(a) is the unidirectional decision variable of the branch road section, as follows:

[0061]

[0062] Step 3063: Calculate P n The safety performance value and efficiency when the road network reaches a balanced state under the current scheme in the present invention are calculated, and the current road network traffic organization scheme is sorted, crossed and mutated iteratively, further comprising the following steps:

[0063] Step 30631: Initialize the iteration count n←0, and use the BPR function to calculate the traffic volume x of each road segment. a The following travel time And calculate the shortest path p between each OD pair w∈W w , record the shortest travel time cost π w , the BPR function calculation formula is:

[0064]

[0065] Among them, under the initial conditions, the traffic flow is zero and the travel time is the zero flow time

[0066] Step 30632: Use all-or-nothing allocation to allocate the shortest path p between each OD pair. w Load travel demand q w , get new road traffic

[0067] Step 30633: Update iteration count n←n+1 and update road traffic Use the BPR function to calculate the travel time under the current traffic flow And calculate the shortest path p between each OD pair w∈W w , record the shortest travel time cost π w ;

[0068] Step 30634: Determine the difference between the traffic volume of the road section calculated for the n+1th time and the traffic volume of the road section calculated for the n+1th time. n Is the difference between the flow results calculated twice less than the calculation error limit set in step 3061? ε ; If it is less than, stop calculating, and then you can get the road section a The final equilibrium flow x a , otherwise return to step 30632;

[0069] Step 30635: Use the calculation formula of the road network safety performance value and traffic efficiency in step 302 to obtain P n The current road network traffic organization scheme is balanced in flow x a The safety cost and time cost under this scheme are used to characterize the road network safety performance and efficiency performance under this scheme; if the multiplication algebra n←0, the current road network scheme is directly stored in R n and record the cost value of the corresponding solution; otherwise, merge the parent population and the child population R n ←P n ∪Q n , store the current solution in the merged R n and record the cost value of the corresponding solution;

[0070] Step 30636: Use the fast non-dominated sorting algorithm to sort R n The individuals are sorted according to the non-dominated level F←(F1,F2,...), and elite individuals are selected, further including the following steps:

[0071] Step 306361: Initialize the parent population P n+1 ←0;

[0072] Step 306362: Place the individuals on the i-th non-dominated level into the parent population one by one n+1 ←P n+1 ∪F i , until the number of individuals in the population reaches the maximum capacity P n+1 |+F i |=M;

[0073] Step 306363: Calculate F using the crowding algorithm i The crowding degree of individuals in

[0074] Step 306364: Select F i Middle front M-|P n+1 | individuals, F i (M-|P n+1 |) and P n+1 Merge P n+1 ←P n+1 ∪Fi (MP n+1 |).

[0075] Step 30637: P n+1 The individuals undergo crossover and mutation to generate the offspring population Q n+1 ;

[0076] Step 30638: Update the multiplication algebra n←n+1;

[0077] Step 30639: If n < N, return to step 3063, otherwise merge the parent population and the child population R n ←P n ∪Q n ;

[0078] Step 3064: Obtain the Pareto front approximation F1 and the optimal strategy set according to the sorting results.

[0079] Preferably, the traffic management requirements of the branch network are analyzed in step 40, and an optimization plan for the one-way traffic organization of the branch network is selected as needed, specifically: from the two aspects of safety assurance and efficiency improvement, the one-way traffic organization requirements of the branch network in the target area are comprehensively analyzed; according to the Pareto front approximation F1 obtained in step 306 and the optimal strategy set, three types of one-way traffic organization optimization plans for the branch network are generated, namely, safety type (focusing on road network safety performance), efficiency type (focusing on road network traffic efficiency) and balance type (significantly improving network security performance without affecting traffic efficiency), and the corresponding plan is selected according to the traffic organization requirements of the branch network.

[0080] Beneficial effects: The present invention makes up for the shortcomings of traditional optimization methods in balancing the overall safety performance and traffic efficiency of the road network. It proposes for the first time an optimization method for one-way traffic organization of urban branch road networks that integrates safety considerations. It can balance the overall safety performance and traffic efficiency of the road network while generating a one-way traffic organization plan with safety and efficiency preference characteristics for selection. This method has reached the world's advanced level. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In the figures, the same reference numerals designate corresponding parts in different views.

[0082] Figure 1 It is a schematic diagram of the road network travel OD matrix shown in an embodiment of the present invention.

[0083] Figure 2 It is a road network topology diagram shown in an embodiment of the present invention.

[0084] Figure 3It is the road section adjacency relationship matrix and the road section-intersection turning connection relationship matrix shown in the embodiment of the present invention.

[0085] Figure 4 It is the structured data of the inherent attribute parameter value of the trunk road shown in the embodiment of the present invention.

[0086] Figure 5 It is the branch inherent attribute parameter value structured data shown in the embodiment of the present invention.

[0087] Figure 6 This is the result of the road segment attribute parameter labeling shown in the embodiment of the present invention.

[0088] Figure 7 This is the intersection attribute parameter labeling result shown in the embodiment of the present invention.

[0089] Figure 8 It is the first randomly generated road network traffic organization plan strategy set shown in the embodiment of the present invention.

[0090] Fig. 9 is the Pareto front approximation shown in the embodiment of the present invention.

[0091] Fig.10 It is a set of optimal strategies for the road network traffic organization scheme shown in the embodiment of the present invention.

[0092] Fig.11 This is the road network traffic organization situation under different schemes shown in the embodiments of the present invention.

[0093] Fig.12 It is a flow chart of the method for optimizing one-way traffic organization in urban road network of the present invention. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. After reading the present invention, those skilled in the art will modify the various equivalent forms of the present invention within the scope defined by the appended claims of the present application.

[0095] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0096] Example

[0097] This embodiment provides a method for optimizing one-way traffic organization in a city branch network. The specific process is as follows: Figure 1 As shown, it mainly includes:

[0098] Step 10: Survey on residents’ travel behavior

[0099] Survey method. This embodiment adopts the RP survey method, specifically a home visit survey, and conducts sample home visits to households within the surveyed city area. The sampling rate can be obtained by querying Table 1 based on the urban population in the survey area. The sampling survey adopts a stratified sampling method, and the implementation steps are:

[0100] According to the existing urban traffic zone division data, traffic zones are divided into several types (levels) based on the land use nature of traffic zones;

[0101] For traffic communities with the same nature, the sample size of each stratum is determined by the community population × sampling rate;

[0102] Conduct random sampling home visits, and investigators will learn about the travel behaviors of all members of the surveyed households in person.

[0103] Table 1 Home visit survey sampling rate query table

[0104]

[0105] The survey content mainly includes: residents’ travel purpose, travel time, travel starting and ending points, etc.

[0106] Data preprocessing. Record the travel demand of each respondent q w , and construct the road network travel OD matrix, such as Figure 2 In the embodiment, a total of 8 OD pairs are set, namely 1-2, 1-4, 2-3, 2-4, 3-1, 3-2, 4-1 and 4-3, and the corresponding initial travel demands are 683, 306, 787, 550, 607, 679, 444 and 584 respectively.

[0107] Step 20: Urban road network parameter survey

[0108] This embodiment needs to obtain the attribute parameter values ​​of the urban branch road network, including the inherent attribute parameter values ​​and the variable attribute parameter values, and draw the road network topology structure according to the actual investigation ( Figure 3 ) and the road section adjacency matrix and the road section-intersection turn connection matrix ( Figure 4 ), the road network of this embodiment includes 248 turns, 32 nodes and 52 road sections, including 18 main road sections and 34 branch road sections.

[0109] The inherent attribute parameter values ​​of the road include: traffic capacity c a , zero flow time Length L a , blocking density Free flow speed Number of lanes n a The data summary format is shown in Table 2, and the structured data of the trunk road inherent attribute parameter value is generated according to Table 2 ( Figure 5) and branch inherent attribute parameter value structured data ( Figure 6 ); In the figure, the first line is the road section number, the second and third lines are the OD pairs, and the fourth line is the traffic capacity c a , the fifth line is the length L a , the sixth line is the zero flow time Line 7 is the free flow speed Line 8: Blockage Density The ninth and tenth rows are the number of lanes n for different driving directions a .

[0110] Variable attribute parameter values ​​include: average traffic flow q i (i=1,…,n), average travel time t i (i=1,…,n), the data summary format is shown in Table 3; the summary format of the number of road section conflicts ka is shown in Table 4, the number of intersection conflicts k a→b The summary format is shown in Table 5.

[0111] Table 2 Summary of inherent attribute parameter values ​​of urban road network

[0112]

[0113] Table 3 Summary of variable attribute parameter values ​​of urban road network

[0114]

[0115] Table 4 Summary of road section conflict times

[0116]

[0117]

[0118] Table 5 Summary of intersection conflicts

[0119]

[0120] According to the summary results of road network parameters, the road section adjacency relationship matrix and the road section-intersection turn connection relationship matrix, the road section and intersection attribute parameters are marked. The results of road section attribute parameter marking are as follows: Figure 7 As shown (with zero flow time For example); the intersection attribute parameter annotation results are as follows Figure 8 As shown, the first line is the turn number, the second and third lines are the road segment numbers connected by the turn, and the fourth line is the turn type, where 1 is right turn, 2 is straight, and 3 is left turn.

[0121] Step 30: Construction, parameter calibration and solution of one-way traffic organization optimization model of urban branch road network

[0122] First, a one-way traffic organization optimization model for the urban branch road network is constructed, which is specifically a two-layer planning model, further including the following steps:

[0123] Step 301: Based on the user balance theory, a branch network balanced traffic assignment model is constructed as a lower layer model, which is as follows:

[0124]

[0125]

[0126] Among them, t a (x) represents the travel time of road section a when the traffic flow is x, f p represents the flow of path p, q w represents the travel demand between OD and w, x a represents the traffic flow of road section a, is the path-segment indication parameter. When segment a is on path p, otherwise

[0127] Step 302: construct an upper model to maximize the road network safety performance (minimize the risk of collision) and minimize the road network traffic efficiency. The objective function is as follows:

[0128]

[0129] minT G (S) = ∑ a∈A t a (x a )×x a

[0130] Among them, R G (S) represents the safety performance value of the road network traffic organization scheme S, T G (S) represents the traffic efficiency of the road network traffic organization plan S;

[0131] The collision risk of a road section is calculated by the following formula:

[0132]

[0133] The intersection collision risk is calculated by the following formula:

[0134]

[0135] B1=b1×k a→b , B2=b2×k a→b

[0136] Among them, b1 and b2 are preset parameters; x a and xa→b Represent the road segments in the road network a The traffic volume at the intersection turning from a to b; t a (x a ) indicates a road segment a The flow rate is x a The passage time of .

[0137] Now calculate the road section collision risk R a (x a ) and road network efficiency T G (S), the specific steps are as follows:

[0138] Step 3021: Calculate the corresponding collision risk according to the road section type. When road section a is a one-way branch road section or a trunk road, that is, a∈A O or a∈A m , the flow rate of vehicle i is x a The road section a will have a collision risk R due to following the vehicle a(i) (x a ) and the risk of collision due to adjacent vehicles changing lanes When section a is a one-way branch section, the specific calculation formula is:

[0139]

[0140] The collision risk parameters include the average collision risk r of vehicle i affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle. a(i) (x a )and Collision risk factor ρ a(i) , average conflict risk threshold λ a(i) (x a )and Risk exposure a(i) (x a )and n a is the number of lanes corresponding to the road section.

[0141] Step 3022: Calculate the collision risk parameter of vehicle i affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle in step 3021, and the calculation formula is:

[0142]

[0143] Among them, D a (x a ) is the flow rate of section a is x a When the expected average distance between vehicles is l i is the length of vehicle i; r is the driver's reaction time; γ is the speed reduction factor (non-negative value); ωis the vehicle's tire-ground friction coefficient; η is the acceleration due to gravity.

[0144] Step 3023: Calculate the flow rate of vehicle i as x a The trunk road a(a∈A m ), the collision risk R caused by following the vehicle a(i) (x a ) and the risk of collision due to adjacent vehicles changing lanes The calculation method is the same as when section a is a one-way branch section, and the calculation formula is:

[0145]

[0146] Step 3024: Calculate the collision risk parameter of vehicle i in step 3023 affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle, and the calculation formula is:

[0147]

[0148]

[0149] Step 3025: Calculate the flow rate of vehicle i as x a The bidirectional branch section a(a∈A T ), the collision risk R caused by following the vehicle a(i) (x a ) and the risk of collision due to adjacent vehicles changing lanes The calculation formula is:

[0150]

[0151] in, Indicates that vehicle i passes through a two-way two-lane branch road (n a =2), the risk of collision due to the adjacent vehicle changing lanes; Indicates that vehicle i passes through a two-way multi-lane branch road (n a >2), the risk of collision due to the adjacent vehicle changing lanes.

[0152] Step 3026: Calculate the collision risk parameter of vehicle i affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle in step 3025, using the following calculation formula:

[0153]

[0154]

[0155] Step 3027: According to the road section travel time t a (x a ), calculate the sum of the travel time of all travelers in the road network∑ a∈A ta (x a )×x a , which is used to characterize the traffic efficiency of the road network.

[0156] Secondly, calibrate the parameters of the one-way traffic organization optimization model of the urban branch road network, including the blocking coefficient of the BPR function α and β, the average expected distance between vehicles D a (x a ) and the average vehicle speed v a , and statistically summarize the road facility parameter values. The specific steps are as follows:

[0157] Step 303: Calibrate the blocking coefficient of the BPR function α and β, the specific steps are as follows:

[0158] Step 3031: construct a parameter fitting function, the calculation formula is:

[0159] Y=β·X+lg(α)

[0160] in,

[0161] Step 3032: Table 3, Summary of Variable Attribute Parameter Values ​​of Urban Road Network for Different Road Sections n Group traffic flow q i and n Group travel time t i Substitute the fitting function of step 3031 into the equation, and we get n A linear equation;

[0162] Step 3033: Based on the result obtained in step 3032 n Linear equations are solved by least squares multivariate linear regression to obtain the blocking coefficients of different road sections. α and β.

[0163] Step 304: Calculate the road section according to the structured data of the inherent attribute parameter values ​​of the urban road network in Table 2 a The flow rate is x a When the average expected distance between vehicles is D a (x a ), the calculation formula is:

[0164]

[0165] Where g represents the average distance between randomly distributed vehicles on the road section (m).

[0166] Step 305: According to the blocking coefficient of step 303 α The calibration results of β and β are used to calculate the road section a The flow rate is x aWhen the average speed of the vehicle is v a , the calculation formula is:

[0167]

[0168] Then, the parameter values ​​of each road facility are summarized and organized into a structured data format as shown in Table 6, and the model parameters are set and organized into a structured data format as shown in Table 7.

[0169] Table 6 Summary of road facility parameter values

[0170]

[0171] Table 7 Model parameter setting table

[0172]

[0173]

[0174] At this point, the parameters and data required for the one-way traffic organization optimization model of the urban branch road network have been calibrated and organized.

[0175] Finally, solve the one-way traffic organization optimization model of the urban branch road network

[0176] Step 306: This embodiment designs a solution algorithm for the one-way traffic organization optimization model of the urban branch road network, specifically a non-dominated classification genetic algorithm, and implements the algorithm through Matlab software. The specific steps are as follows:

[0177] Step 3061: Model parameter input

[0178] Including residents' travel OD matrix ( Figure 2 ), road segment parameters c a , L a , and n a ( Figure 5 and Figure 6 ), road facility parameter values ​​α, β, B1 and B2 (Table 6), model parameter setting values ​​r, γ, ω ,η,m,l,N,M,p c 、p m and ε (Table 7).

[0179] Step 3062: Randomly generate a road network traffic organization plan

[0180] Set the initial generation number n←0 to generate the parent population P n (P n ={S1,S2,…,S n}), the parent population is composed of randomly generated road network traffic organization scheme S k Fill composition, set the offspring population Where S k ={s(a),a∈A O ∪A T}(k=1,…,n), s(a) is the unidirectional decision variable of the branch road section, as follows:

[0181]

[0182] The road network traffic organization plan strategy set randomly generated in the first iteration is as follows: Fig. 9 As shown, each row represents a solution.

[0183] Step 3063: Calculate the safety performance value and efficiency of the current road network solution, and perform sorting, crossover and mutation iteration operations. The specific steps are as follows:

[0184] Step 30631: Initialize the iteration count n←0, and use the BPR function to calculate the traffic volume x of each road segment. a The following travel time And calculate the shortest path p between each OD pair w∈W w , record the shortest travel time cost π w , the BPR function calculation formula is:

[0185]

[0186] Among them, under the initial conditions, the traffic flow is zero and the travel time is the zero flow time

[0187] Step 30632: Use all-or-nothing allocation to allocate the shortest path p between each OD pair. w Load travel demand q w , get new road traffic

[0188] Step 30633: Update iteration count n←n+1 and update road traffic Use the BPR function to calculate the travel time under the current traffic flow And calculate the shortest path p between each OD pair w∈W w , record the shortest travel time cost π w ;

[0189] Step 30634: Determine the difference between the traffic volume of the road section calculated for the n+1th time and the traffic volume of the road section calculated for the n+1th time. n Is the difference between the flow results calculated twice less than the calculation error limit set in step 3061? ε ; If it is less than, stop calculating, and then you can get the road section aThe final equilibrium flow x a , otherwise return to step 30632;

[0190] Step 30635: Use the calculation formula of the road network safety performance value and traffic efficiency in step 302 to obtain P n The current road network traffic organization scheme is balanced in flow x a The safety cost and time cost under the multiplication algebra n←0, the current road network plan is directly stored in R n and record the cost value of the corresponding solution; otherwise, merge the parent population and the child population R n ←P n ∪Q n , store the current solution in the merged R n and record the cost value of the corresponding solution;

[0191] Step 30636: Use the fast non-dominated sorting algorithm to sort R n The individuals are sorted according to the non-dominated level F←(F1,F2,...), and elite individuals are selected, further including the following steps:

[0192] Step 306361: Initialize the parent population P n+1 ←0;

[0193] Step 306362: Place the individuals on the i-th non-dominated level into the parent population one by one n+1 ←P n+1 ∪F i , until the number of individuals in the population reaches the maximum capacity |P n+1 |+|F i |=M;

[0194] Step 306363: Calculate F using the crowding algorithm i The crowding degree of individuals in

[0195] Step 306364: Select F i Middle front M-|P n+1 | individuals, F i (M-|P n+1 |) and P n+1 Merge P n+1 ←P n+1 ∪F i (M-|P n+1 |).

[0196] Step 30637: P n+1 The individuals undergo crossover and mutation to generate the offspring population Q n+1 ;

[0197] Step 30638: Update the multiplication algebra n←n+1;

[0198] Step 30639: If n < N, return to step 3063, otherwise merge the parent population and the child population R n ←P n ∪Q n ;

[0199] Step 3064: Obtain the Pareto front approximation F1 ( Fig.10 ) and the optimal strategy set ( Fig.11 ).

[0200] Step 104: Analyze the traffic management requirements of the branch network and select the branch network one-way traffic organization optimization plan as needed

[0201] According to the Pareto front approximation F1 obtained in step 306 and the optimal strategy set, three branch network one-way traffic organization optimization schemes are generated, namely, safety type (focusing on road network safety performance), efficiency type (focusing on road network traffic efficiency) and balance type (significantly improving network security performance without affecting traffic efficiency), and the corresponding scheme is selected according to the traffic organization needs of the branch network.

[0202] Among them, if the goal of one-way traffic organization is to maximize traffic efficiency while ensuring a certain level of safety, then the efficiency optimization scheme is selected, and its corresponding one-way traffic organization is as follows: Fig.12 a; if the one-way traffic organization goal of the branch network is to ensure both traffic safety and efficiency, then the balanced optimization scheme is selected, and the corresponding one-way traffic organization is as follows Fig.12 b; if the purpose of one-way traffic organization is to improve the safety performance of the road network, then the safety optimization scheme is selected, and the corresponding one-way traffic organization is as follows Fig.12 As shown in c.

[0203] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0204] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0205] Although the present invention has been described above with reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the present invention. Therefore, it is intended that the above detailed description is considered to be illustrative rather than restrictive, and it should be understood that the following claims (including all equivalents) are intended to limit the spirit and scope of the present invention. The above embodiments should be understood to be only used to illustrate the present invention and not to limit the scope of protection of the present invention. After reading the content of the record of the present invention, the technician can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall into the scope defined by the claims of the present invention.

Claims

1. A method for optimizing one-way traffic organization of urban branch road network, characterized in that: The following steps are involved: Step 10: Conduct a survey on residents’ travel behavior to obtain residents’ travel demand data; Step 20: Survey urban road network parameters, obtain attribute parameter values ​​of urban branch road network and construct road section adjacency relationship matrix and road section-intersection turn connection relationship matrix; Step 30: Construction, parameter calibration and solution of one-way traffic organization optimization model of urban branch road network; Step 40: Analyze the traffic management requirements of the branch network and select a one-way traffic organization optimization plan for the branch network as needed.

2. The method for optimizing one-way traffic organization of urban branch road network according to claim 1 is characterized in that: The step 10 specifically includes: Step 101: sampling the survey subjects, wherein the sampling adopts stratified sampling; Step 102: Use the RP survey method to conduct a survey on residents’ travel needs. The survey content includes: Assuming that there are w types of residents with different travel needs in the area, investigate the travel needs q of each type of residents. w , and construct the road network travel OD matrix.

3. The method for optimizing one-way traffic organization of urban branch road network according to claim 1 is characterized in that: The step 20 specifically includes: Step 201: Obtain the attribute parameter values ​​of each road section and intersection within the time period T=nt, including the inherent attribute parameter values: traffic capacity c a , Zero flow time Length L a , blocking density Free flow speed Variable attribute parameter value: average traffic flow q i , average travel time t i , i = 1, ..., n; number of road section conflicts k a , intersection conflict times k a→b , vehicle mass m, and organize it into a structured data format; Step 202: Draw a road network topology diagram According to the inter-cell accessibility relationship shown in the structure diagram, the road section adjacency relationship matrix and the road section-intersection turn connection relationship matrix are constructed, and the road section and intersection attribute parameters are marked; among them, the node set V = (v1,…,vq), the road section arc A = A O ∪A T ∪A m , the road segment arc A includes the one-way branch road segment arc set A O , Bidirectional branch road section set A T and main line segment arc A m , is the intersection turning arc set.

4. The method for optimizing one-way traffic organization of urban branch road network according to claim 1, characterized in that: The construction of the one-way traffic organization optimization model of the urban branch road network in step 30 is specifically a two-layer planning model, including: Step 301: Based on the user balance theory, a branch network balanced traffic assignment model is constructed as a lower layer model: Among them, t a (x) represents the travel time of road section a when the traffic flow is x, f p represents the flow of path p, q w represents the travel demand between OD and w, x a represents the traffic flow of road section a, is the path-segment indication parameter. When segment a is on path p, otherwise Step 302: Construct an upper model to maximize the road network safety performance, that is, minimize the collision risk and minimize the road network traffic efficiency: minT G (S)=∑ a∈A t a (x a )×x a Among them, R G (S) represents the safety performance value of the road network traffic organization scheme S, T G (S) represents the traffic efficiency of the road network traffic organization plan S; The collision risk of a road segment is calculated by the following formula: The intersection collision risk is calculated by the following formula: R a→b (x a→b )=B1x a→b 2 +B2x a→b , <h2 style=";text-align:left;direction:ltr">B1 = b1×k<h2 style=";text-align:left;direction:ltr"> a→b <h2 style=";text-align:left;direction:ltr"> B2 = b2×k<h2 style=";text-align:left;direction:ltr"> a→b Among them, b1 and b2 are preset parameters; x a and x a→b They represent the traffic volume of the road section a and the intersection turning a→b in the road network respectively; t a (x a ) indicates that the flow rate of section a is x a The passage time of .

5. The method for optimizing one-way traffic organization of urban branch road network according to claim 4 is characterized in that: The road section collision risk R in step 302 a (x a ) and road network efficiency T G The calculation of (S) includes: Step 3021: Calculate the corresponding collision risk according to the road section type. When road section a is a one-way branch road section or a trunk road, that is, a∈A O or a∈A m , the flow rate of vehicle i is x a The road section a generates a collision risk R due to following vehicles. a(i) (x a ) and the risk of collision due to adjacent vehicles changing lanes The calculation formula is: R a(i) (x a )=ρ a(i) (v a )×r a(i) (x a )×e a(i) (x a )×δi The collision risk parameters include the average collision risk r of vehicle i affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle. a(i) (x a )and Collision risk factor ρ a(i) , average conflict risk threshold λ a(i) (x a )and Risk exposure a(i) (x a )and n a is the number of lanes corresponding to the road section; Step 3022: Calculate the collision risk parameter of vehicle i affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle in step 3021, using the following calculation formula: e a(i) (x a )=t a ×k a ×x a Among them, D a (x a ) is the flow rate of section a is x a When , the expected average distance between vehicles; l i is the length of vehicle i; r is the driver's reaction time; γ is the speed reduction factor; ω is the vehicle's tire-ground friction coefficient; η is the acceleration due to gravity; Step 3023: When section a is a bidirectional branch section, a∈A T , the flow rate of vehicle i is x a The collision risk R caused by following a vehicle a(i) (x a ) and the risk of collision due to adjacent vehicles changing lanes The calculation formula is: R a(i) (x a )=ρ a(i) (v a )×r a(i) (x a )×e a(i) (x a )×δi in, It means that when vehicle i passes through a two-way two-lane branch road, n a =2, collision risk due to the neighboring vehicle changing lanes; When vehicle i passes through a two-way multi-lane branch road, n a >2, collision risk caused by the neighboring vehicle changing lanes; Step 3024: Calculate the collision risk parameter of vehicle i in step 3023 affected by the braking of the preceding vehicle and the lane change of the adjacent vehicle, and the calculation formula is: e a(i) (x a )=t a ×k a ×x a Step 3025: According to the road section travel time t a (x a ), calculate the sum of the travel time of all travelers in the road network∑ a∈A t a (x a )×x a , which is used to characterize the traffic efficiency of the road network.

6. The method for optimizing one-way traffic organization of urban branch road network according to claim 1, characterized in that: The calibration of the parameters of the urban branch road network one-way traffic organization optimization model in step 30 includes: Step 303: Calibrate the blocking coefficients α and β of the BPR function according to the urban branch network attribute parameter values ​​obtained in step 201, further comprising the following steps: Step 3031: construct a parameter fitting function, the calculation formula is: Y=β·X+lg(α) in, Step 3032: The n groups of traffic flow q obtained by the investigation in step 102 are i and n groups of travel times t i The structured data is brought into the fitting function of step 3031 to obtain n linear equations; Step 3033: according to the n linear equations obtained in step 3032, least squares multivariate linear regression is used to solve them and the blocking coefficients α and β are obtained; Step 304: Based on the urban branch network attribute parameter values ​​obtained in step 201, calculate the flow rate of section a as x a When the average expected distance between vehicles is D a (x a ), the calculation formula is: Where g represents the average distance between randomly distributed vehicles on the road section; Step 305: Based on the calibration results of the resistance coefficients α and β in step 303, the flow rate of section a is calculated as x a When the average speed of the vehicle is v a , the calculation formula is:

7. The method for optimizing one-way traffic organization of urban branch road network according to claim 1 is characterized in that: The solution of the one-way traffic organization optimization model of the urban branch road network in step 30 includes: Step 306: using a non-dominated classification genetic algorithm to solve the one-way traffic organization optimization model of the urban branch network, obtaining a Pareto front approximation F1 and a better strategy set, further comprising the following steps: Step 3061: Input the initial data of the model, including the travel demand q obtained in step 101 w , the road section parameters obtained in step 201 c a , L a , and At the same time, set the model's maximum reproduction generation number N, population size M, and crossover probability p c , mutation probability p m , calculate the error limit ε; Step 3062: Set the initial generation number n←0 to generate the parent population P n (P n ={S1,S2,…,S n }), the parent population is composed of randomly generated road network traffic organization scheme S k Fill composition, set the offspring population Where S k ={s(a),a∈A O ∪A T }(k=1,…,n), s(a) is the unidirectional decision variable of the branch road section, as follows: Step 3063: Calculate P n The safety performance value and efficiency performance value when the road network reaches a balanced state under the current scheme, and the current road network traffic organization scheme is sorted, crossed and mutated iteratively; Step 3064: Obtain the Pareto front approximation F1 and the optimal strategy set according to the sorting results.

8. The method for optimizing one-way traffic organization of urban branch road network according to claim 7 is characterized in that: The step 3063 includes: Step 30631: Initialize the iteration count n←0, and use the BPR function to calculate the traffic volume x of each road segment. a The following travel time And calculate the shortest path p between each OD pair w∈W w , record the shortest travel time cost π w , the BPR function calculation formula is: Among them, under the initial conditions, the traffic flow is zero and the travel time is the zero flow time Step 30632: Use all-or-nothing allocation to allocate the shortest path p between each OD pair. w Load travel demand q w , get new road traffic Step 30633: Update iteration count n←n+1 and update road traffic Use the BPR function to calculate the travel time under the current traffic flow And calculate the shortest path p between each OD pair w∈W w , record the shortest travel time cost π w ; Step 30634: Determine whether the difference between the traffic flow calculated for the n+1th time and the traffic flow calculated for the nth time is less than the calculation error limit ε set in step 3061; if it is less, stop the calculation, and the final balanced traffic flow x of section a can be obtained. a , otherwise return to step 30632; Step 30635: Use the calculation formula of the road network safety performance value and traffic efficiency in step 302 to obtain P n The current road network traffic organization scheme is balanced in flow x a The safety cost and time cost under this scheme are used to characterize the road network safety performance and efficiency performance under this scheme; if the multiplication algebra n←0, the current road network scheme is directly stored in R n and record the cost value of the corresponding solution; otherwise, merge the parent population and the child population R n ←P n ∪Q n , store the current solution in the merged R n and record the cost value of the corresponding solution; Step 30636: Use the fast non-dominated sorting algorithm to sort R n The individuals are sorted according to the non-dominated level F←(F1,F2,...), and the elite individuals are selected.

9. The method for optimizing one-way traffic organization of urban branch road network according to claim 1, characterized in that: The step 40 is specifically as follows: comprehensively analyzing the one-way traffic organization needs of the branch network in the target area from the two aspects of safety assurance and efficiency improvement; generating three branch network one-way traffic organization optimization schemes of safety, efficiency and balance according to the Pareto front approximation F1 and the optimal strategy set obtained in step 306, and selecting the corresponding scheme according to the traffic organization needs of the branch network.

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