Urban mixed traffic flow fundamental diagram model analysis method and system in Internet of Vehicles environment

Abstract

The invention provides an urban mixed traffic flow fundamental diagram model analysis method and system in an Internet of Vehicles environment, and belongs to the technical field of urban traffic management and networked automatic driving automobile application. Different car-following models are selected according to car-following vehicle types, and meanwhile, nine different time headway types of front and back vehicle types are set; based on a Markov chain model, calculating a transition probability corresponding to each vehicle following mode in the urban mixed traffic flow, deducing a basic diagram model of the mixed traffic flow under a single lane, and considering the influence of the network connection automatic driving vehicle permeability, the bus permeability and the network connection automatic driving vehicle queuing intensity on the road traffic capacity; the influence of different influence factors on the flow stability of the mixed traffic flow is analyzed under the consideration of the influence of the random time headway. According to the invention, the road traffic capacity under different traffic densities and vehicle type distribution conditions is disclosed; the influence of the random time headway on the traffic flow stability is discussed under the existing basic diagram model framework.

Description

Technical Field

[0001] The present invention relates to the field of urban traffic management and application technology of networked automatic vehicles, and in particular to a method and system for analyzing a basic graph model of urban mixed traffic flow that takes into account the characteristics of intelligent networked vehicles and buses. Background Art

[0002] The Connected and Automated Vehicle (CAV) combines the cutting-edge technologies of Connected Vehicle (CV) and Autonomous Vehicles (AV). It can not only assist and automate vehicle driving tasks, but also communicate with the surrounding environment and other vehicles in real time. This high degree of information exchange is a huge advantage in improving road capacity, enhancing flow stability and improving travel convenience. In theory, when all vehicles on the road are CAVs, the headway will be significantly shortened because vehicles can be precisely coordinated through real-time communication, and road utilization and traffic efficiency will reach the optimal level.

[0003] In the evolution of urban transportation systems, many countries tend to adopt and implement a bus-dominated urban transportation development model. With the continuous development of vehicle networking technology, urban roads will be in a mixed traffic scene of CAVs, HVs and buses for a long time in the future. The different response times caused by their technical differences may increase the instability of traffic flow. In this mixed traffic scenario, due to the significant differences in vehicle response time, operation mode, headway, etc., traffic flow instability may occur. Therefore, before unmanned driving technology becomes mainstream and widely used, how to effectively manage and optimize urban mixed traffic flow has become an urgent problem to be solved in the field of transportation research. This requires in-depth analysis of the interaction mechanism between CAVs, HVs and buses at different penetration rates, and designing management and control strategies that can optimize traffic flow in mixed traffic scenarios based on this complex traffic flow characteristics. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for analyzing a basic graph model of urban mixed traffic flow in a vehicle networking environment, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for analyzing a basic graph model of urban mixed traffic flow taking into account the characteristics of intelligent connected vehicles and buses, comprising the following steps:

[0007] S1. Select different following models according to the type of following vehicle, and set nine different headway types for different front and rear vehicle types;

[0008] S2. Based on the Markov chain model, the transition probability corresponding to each following mode in the urban mixed traffic flow is calculated;

[0009] S3. Based on the transition probability of step S2, derive the basic graph model of mixed traffic flow under a single lane, considering the impact of the penetration rate of connected autonomous vehicles, the penetration rate of buses, and the intensity of the queues of connected autonomous vehicles on the road capacity;

[0010] S4. Considering the influence of random headway, the influence of different influencing factors on the flow stability of mixed traffic flow is analyzed.

[0011] As a further limitation of the first aspect of the present invention, in step S1, the headway is divided into the following nine types according to the types of the front and rear vehicles: 1) h cc Indicates the headway time between a CAV and another CAV; 2)h ch Indicates the headway time between the CAV and the HV; 3)h cb Indicates the headway time of the CAV following the bus; 4)h hc Indicates the headway time of the HV following the CAV; 5)h hh Indicates the headway time of an HV following another HV; 6)h hb Indicates the headway time of the HV following the bus; 7)h bc Indicates the headway time of the bus following the CAV; 8)h bh Indicates the headway time of the bus following the HV; 9)h bb Indicates the headway time when a bus is following another bus.

[0012] The following models corresponding to different following vehicle types are: 1) When the following vehicle is an intelligent connected vehicle, the corresponding following model is: n =l+h C v n ; Among them, s n is the distance between the front of the nth vehicle and the rear bumper of the preceding vehicle, l is the length of the nth vehicle, v n is the speed of the nth vehicle, h C is the time distance between the nth vehicle and the rear bumper of the preceding vehicle. 2) When the following vehicle is a human-driven vehicle, the corresponding following model is: s n =l+s0+v n h H ; where l and v nThey refer to the length and speed of the nth HV vehicle, s0 refers to the minimum safe distance that the nth vehicle should maintain with the vehicle in front, and h H Refers to the constant time distance between the nth vehicle and the rear bumper of the preceding vehicle. 3) When the following vehicle is a bus, the corresponding following model is: s n =l'+s1+v n h B ; where l' and v n They refer to the length and speed of the nth bus, s1 refers to the minimum safe distance that the nth bus should maintain with the preceding bus, and h B Refers to the set constant time distance between the nth vehicle and the rear bumper of the vehicle in front.

[0013] As a further limitation of the first aspect of the present invention, the specific calculation steps of step S2 based on the Markov chain model are: S2.1, define the state space of the discrete Markov chain used to describe the headway transfer as S = {-1, 0, 1}, where -1 represents a bus, 0 represents an HV, and 1 represents a CAV; S2.2, define the form of the transfer matrix T of the discrete Markov chain used to describe the headway transfer; S2.3, define the transfer probability values ​​of each item in the transfer matrix T of the discrete Markov chain according to the characteristics of the mixed traffic flow;

[0014] As a further limitation of the first aspect of the present invention, step S3 is as follows: S3.1. Under mixed urban traffic flow, set h mn (m,n∈S) is the expected headway of nine different types of headways. The total headway of the mixed traffic flow is calculated to obtain the density of a single lane. S3.2. The road operation state is divided into a free flow state and a congested flow state, and the maximum capacity function of a single lane is calculated.

[0015] As a further limitation of the first aspect of the present invention, considering the influence of random headway on traffic flow, the steps are as follows: let T represent the running time of the traffic flow, determine the number of vehicles that can pass in a fixed time T; calculate the mean and variance of the mixed traffic flow when T→∞, assuming that each headway h mn have a finite mean μ mn and variance Determine the mean and variance of the total headway h", determine the mean and variance of the asymptotic normal distribution obeyed by the number of vehicles that can pass in a fixed time T, and determine the mean and variance of the asymptotic normal distribution obeyed by the number of vehicles passing through the road section per unit time.

[0016] In a second aspect, the present invention provides a basic graph model analysis system for urban mixed traffic flow considering the characteristics of intelligent connected vehicles and buses, comprising the following steps:

[0017] A selection and determination module is used to select different following models according to the type of the following vehicle, and to set nine headway types with different types of front and rear vehicles;

[0018] A calculation module is used to calculate the transfer probability corresponding to each following mode in the urban mixed traffic flow based on the Markov chain model;

[0019] The derivation and determination module is used to derive the basic graph model of mixed traffic flow under a single lane based on the transition probability, and determine the impact of the penetration rate of connected autonomous vehicles, the penetration rate of buses, and the intensity of the queues of connected autonomous vehicles on the road capacity;

[0020] The analysis module is used to analyze the impact of different influencing factors on the flow stability of mixed traffic flow under the influence of random headway.

[0021] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the basic graph model analysis method of urban mixed traffic flow in a vehicle network environment as described in the first aspect is implemented.

[0022] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the basic graph model analysis method for urban mixed traffic flow in a vehicle networking environment as described in the first aspect.

[0023] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the basic graph model analysis method of urban mixed traffic flow in a vehicle networking environment as described in the first aspect.

[0024] The beneficial effects of the present invention are as follows: in view of the mixed traffic scenarios of multiple types of vehicles on urban roads, a basic graph model considering the vehicle arrangement order based on the Markov chain model is established, and the road capacity under different traffic density and vehicle type distribution conditions is revealed by analyzing the influence of CAV penetration rate, bus penetration rate and CAV queuing intensity on the basic graph of traffic flow; further, under the existing basic graph model framework, the influence of random headway on traffic flow stability is discussed, revealing the important role of headway on traffic flow fluctuation and system stability.

[0025] Additional advantages of the present invention will be more clearly presented in the following description or learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of 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.

[0027] Figure 1 This is a schematic flow chart of a method for analyzing a basic graph model of urban mixed traffic flow taking into account the characteristics of intelligent connected vehicles and buses according to an embodiment of the present invention.

[0028] Figure 2 A schematic diagram of various types of headway distances and CAV queue strengths as specified in an embodiment of the present invention.

[0029] Figure 3 It is a schematic diagram of parameter sensitivity flow-density results under different CAV permeabilities in the basic graph model described in an embodiment of the present invention.

[0030] Figure 4 It is a schematic diagram of parameter sensitivity flow-density results under different bus penetration rates in the basic graph model described in an embodiment of the present invention.

[0031] Figure 5 It is a schematic diagram of parameter sensitivity flow-density results under different CAV queuing intensities in the basic graph model described in an embodiment of the present invention.

[0032] Figure 6 This is a schematic diagram of the sensitivity of different CAV penetration rates to flow stability under the influence of random vehicle heads according to an embodiment of the present invention.

[0033] Figure 7 This is a schematic diagram of the sensitivity of different bus penetration rates to flow stability under the influence of random vehicle heads according to an embodiment of the present invention.

[0034] Figure 8 This is a schematic diagram of the sensitivity of different CAV queue intensities to flow stability under the influence of random vehicle heads according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below by the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0036] It should be understood by those skilled in the art that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0037] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. Different embodiments or examples described in this specification and features of different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

[0038] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0039] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0040] The present invention provides a method for analyzing a basic graph model of urban mixed traffic flow that takes into account the characteristics of intelligent connected vehicles and buses. Taking into account the random arrangement order between mixed vehicles, the influence of different intelligent connected vehicle penetration rates on the traffic capacity of mixed sections is studied. On this basis, a method for studying the stability of road flow caused by random headway time corresponding to different following vehicle types is further proposed, providing certain theoretical support for future traffic planning. The present invention constructs a macro basic graph of urban roads of multiple types of vehicles including autonomous driving vehicles (CAV), traditional human-driven vehicles (HV) and public buses based on Markov chains to depict the interactive relationship between multiple traffic entities in complex urban roads. By deeply analyzing the behavioral characteristics of different vehicle types in traffic flow and their mutual influence, a macro perspective is provided for traffic flow modeling in complex mixed traffic environments, and a theoretical basis is provided for subsequent traffic flow prediction, traffic management and optimization strategies.

[0041] Example 1

[0042] In this embodiment 1, first, a basic graph model analysis system for urban mixed traffic flow in a connected vehicle environment is provided, including: a selection and determination module, which is used to select different following models according to the type of following vehicle, and set nine headway types with different front and rear vehicle types; a calculation module, which is used to calculate the transfer probability corresponding to each following mode in the urban mixed traffic flow based on the Markov chain model; a derivation and determination module, which is used to derive the basic graph model of the mixed traffic flow under a single lane based on the transfer probability, and determine the influence of the penetration rate of connected autonomous driving vehicles, the penetration rate of buses and the queuing intensity of connected autonomous driving vehicles on the road capacity; an analysis module, which is used to analyze the influence of different influencing factors on the flow stability of the mixed traffic flow under the influence of random headway.

[0043] In this embodiment 1, based on the above system, a basic graph model analysis method for urban mixed traffic flow considering the characteristics of intelligent networked vehicles and buses is implemented, and its steps include: selecting different following models according to the type of following vehicles, and setting nine headway types with different front and rear vehicle types; calculating the transfer probability corresponding to each following mode in the urban mixed traffic flow based on the Markov chain model; deriving the basic graph model of mixed traffic flow under a single lane, considering the impact of the penetration rate of networked autonomous driving vehicles, the penetration rate of buses and the queuing intensity of networked autonomous driving vehicles on the road capacity; considering the impact of different influencing factors on the flow stability of mixed traffic flow under the influence of random headway. In this embodiment, the impact of different vehicle penetration rates and queuing intensities on road flow in an environment where multiple vehicle types are mixed, as well as the impact of the randomness of headway on the stability of the basic graph are studied, providing a theoretical basis for future traffic guidance.

[0044] In this embodiment, the headway is divided into the following nine types according to the different types of front and rear vehicles: 1) h cc Indicates the headway time between a CAV and another CAV; 2)h ch Indicates the headway time between the CAV and the HV; 3)h cb Indicates the headway time of the CAV following the bus; 4)h hc Indicates the headway time of the HV following the CAV; 5)h hh Indicates the headway time of an HV following another HV; 6)h hb Indicates the headway time of the HV following the bus; 7)h bc Indicates the headway time of the bus following the CAV; 8)h bh Indicates the headway time of the bus following the HV; 9)h bb Indicates the headway time when a bus is following another bus.

[0045] The following models corresponding to different following vehicle types are: 1) When the following vehicle is an intelligent connected vehicle, the corresponding following model is: n =l+h C v n , where s n is the distance between the front of the nth vehicle and the rear bumper of the preceding vehicle, l is the length of the nth vehicle, v n is the speed of the nth vehicle, h C is the time distance between the nth vehicle and the rear bumper of the preceding vehicle. 2) When the following vehicle is a human-driven vehicle, the corresponding following model is: s n =l+s0+v n h H , where l and v n They refer to the length and speed of the nth HV vehicle, s0 refers to the minimum safe distance that the nth vehicle should maintain with the vehicle in front, and h H Refers to the constant time distance between the nth vehicle and the rear bumper of the preceding vehicle. 3) When the following vehicle is a bus, the corresponding following model is: s n =l'+s1+v n h B , where l' and v n They refer to the length and speed of the nth bus, s1 refers to the minimum safe distance that the nth bus should maintain with the preceding bus, and h B Refers to the set constant time distance between the nth vehicle and the rear bumper of the vehicle in front.

[0046] In this embodiment, in the process of establishing the Markov chain, the state space of the discrete Markov chain used to describe the headway transfer is first defined as S={C, B, H}, where B represents a bus, H represents an HV, and C represents a CAV; then the transfer matrix T of the discrete Markov chain used to describe the headway transfer is defined as:

[0047]

[0048] where t sk represents the probability that a type s vehicle is followed by a type k vehicle, i.e., t sk =P(X n+1 =k|X n =s), n∈{0,1,...,N-1};

[0049] According to the characteristics of mixed traffic flow, the transition probability values ​​of each item in the transition matrix T of the discrete Markov chain are defined as:

[0050] When the first vehicle is a BUS type vehicle, the probability of different following vehicle types is:

[0051]

[0052]

[0053] When the first vehicle is an HV type vehicle, the probability of different following vehicle types is:

[0054]

[0055] When the first vehicle is a CAV type vehicle, the probability of different following vehicle types is:

[0056]

[0057] t CC (P C ,P B ,O)=1-t CH (P C ,P B ,O)-t CB (P C ,P B ,O)

[0058] Among them, P C refers to the proportion of CAV vehicles in mixed traffic flow, P B It refers to the proportion of buses in the mixed traffic flow, and O refers to the queue intensity of CAV vehicles in the traffic flow, with a value range of [-1,1].

[0059] In this embodiment, the urban road operation state is divided into a free flow state and a congested flow state, and the maximum traffic capacity function of a single lane is calculated as follows:

[0060] 1) Free flow state

[0061] q=kv f

[0062] Among them, v f is the free flow density.

[0063] 2) Congestion flow status

[0064]

[0065] Among them, l is the body length of CAV and HV vehicles; l' is the body length of bus vehicles; s0 is the minimum stopping distance of HV vehicles; s1 is the minimum stopping distance of buses.

[0066] In this embodiment, the influence of random headway on traffic flow is also considered. Let T represent the running time of traffic flow. The number of vehicles N(T) that can pass in a fixed time T is:

[0067]

[0068] in, Represents the mean of all types of headway times.

[0069] Next, we calculate the mean and variance of mixed traffic flow when T→∞, assuming that each headway h mn have a finite mean μ mn and variance make represents the equivalent headway, then the mean and variance of the total headway h” are:

[0070]

[0071] According to the central limit theorem, when N→∞, we have:

[0072]

[0073] Therefore, when T→∞, the mean and variance of the asymptotic normal distribution of N(T) are:

[0074]

[0075] Because q represents the number of vehicles passing through the road section per unit time, N(T) and q obey the relationship:

[0076]

[0077] Correspondingly, the mean and variance of the asymptotic normal distribution that q obeys are:

[0078]

[0079] In this embodiment, it is necessary to derive a road capacity model based on a Markov chain and use software such as Matlab to perform sensitivity analysis.

[0080] Example 2

[0081] like Figure 1 As shown, in this embodiment 2, a basic graph model analysis method for urban mixed traffic flow considering the characteristics of intelligent networked vehicles and buses is provided. It includes the following process steps:

[0082] S1. Select different following models according to the type of vehicle being followed, and set nine different headway types for different types of vehicles:

[0083] According to the different types of front and rear vehicles, the headway is divided into the following nine types: Figure 2 As shown: 1)h cc Indicates the headway time between a CAV and another CAV; 2)h ch Indicates the headway time between the CAV and the HV; 3)h cbIndicates the headway time of the CAV following the bus; 4)h hc Indicates the headway time of the HV following the CAV; 5)h hh Indicates the headway time of an HV following another HV; 6)h hb Indicates the headway time of the HV following the bus; 7)h bc Indicates the headway time of the bus following the CAV; 8)h bh Indicates the headway time of the bus following the HV; 9)h bb Indicates the headway time of a bus following another bus

[0084] The following models corresponding to different following vehicle types are:

[0085] 1) When the following vehicle is an intelligent connected vehicle, the corresponding following model is:

[0086] s n =l+h C v n

[0087] Among them, s n is the distance between the front of the nth vehicle and the rear bumper of the preceding vehicle, l is the length of the nth vehicle, which is 4.5 m, and v n is the speed of the nth vehicle, h C is the time distance between the nth vehicle and the rear bumper of the preceding vehicle.

[0088] 2) When the following vehicle is a human-driven vehicle, the corresponding following model is:

[0089] s n =l+s0+v n h H

[0090] Among them, l and v n They are the length and speed of the nth HV vehicle, l is 4.5m, s0 is the minimum safe distance that the nth vehicle should maintain with the vehicle in front, which is 2m, and h H Refers to the set constant time distance between the nth vehicle and the rear bumper of the vehicle in front.

[0091] 3) When the following vehicle is a bus, the corresponding following model is:

[0092] s n =l'+s1+v n h B

[0093] Among them, l' and v n They refer to the length and speed of the nth bus respectively, l' is 12m, s1 refers to the minimum safe distance that the nth bus should maintain with the preceding bus, which is 3m, and hB Refers to the set constant time distance between the nth vehicle and the rear bumper of the vehicle in front.

[0094] S2. Based on the Markov chain model, the transition probability corresponding to each following mode in the urban mixed traffic flow is calculated:

[0095] S2.1. Define the state space of the discrete Markov chain used to describe the headway transfer as S = {-1, 0, 1}, where -1 represents a bus, 0 represents a HV, and 1 represents a CAV;

[0096] S2.2. The form of the transfer matrix T of the discrete Markov chain used to describe the headway transfer is defined as:

[0097]

[0098] where t sk represents the probability that a type s vehicle is followed by a type k vehicle, i.e., t sk =P(X n+1 =k|X n =s), n∈{0,1,...,N-1};

[0099] S2.3. According to the characteristics of mixed traffic flow, define the transition probability values ​​of each item in the transition matrix T of the discrete Markov chain:

[0100] When the first vehicle is a BUS type vehicle, the probability of different following vehicle types is:

[0101]

[0102] When the first vehicle is an HV type vehicle, the probability of different following vehicle types is:

[0103]

[0104] When the first vehicle is a CAV type vehicle, the probability of different following vehicle types is:

[0105]

[0106] t CC (P C ,P B ,O)=1-t CH (P C ,P B ,O)-t CB (P C ,P B ,O)

[0107] Among them, P C refers to the proportion of CAV vehicles in mixed traffic flow, P BIt refers to the proportion of buses in the mixed traffic flow, and O refers to the queue intensity of CAV vehicles in the traffic flow, with a value range of [-1,1].

[0108] S3. Based on the transition probability of step S2, the basic graph model of mixed traffic flow under a single lane is derived, considering the impact of the penetration rate of connected autonomous driving vehicles, the penetration rate of buses, and the intensity of the queues of connected autonomous driving vehicles on the road capacity:

[0109] S3.1. In mixed urban traffic flow, set h mn (m,n∈S) is the expected headway of nine different types of headways. The total headway of mixed traffic flow is calculated, and the density of a single lane is obtained as:

[0110]

[0111] Among them, m,n∈S={-1,0,1}, N is the total number of mixed vehicles.

[0112] S3.2. Divide the road operation state into free flow and congestion flow, and calculate the maximum traffic capacity function of a single lane as follows: Free flow state: q = kv f , where v f is the free flow density, which is 80km / h. Congestion flow state: Among them, l is the body length of CAV and HV vehicles; l' is the body length of bus vehicles; s0 is the minimum stopping distance of HV vehicles; s1 is the minimum stopping distance of buses.

[0113] According to the above basic graph model, the sensitivity analysis of penetration rate, convoy intensity and free flow speed is carried out. It is worth noting that the values ​​of the nine headways are assumed to be constant, as shown in Table 1 below.

[0114] Table 1

[0115]

[0116] like Figure 3 As shown in the figure, a parameter sensitivity analysis of CAV penetration is performed, setting the free flow speed to 80 km / h, the bus penetration rate to 0, the CAV queue intensity to 0, and P cThe values ​​are 0, 0.25, 0.5, 0.75, and 1 respectively, and the influence of different CAV penetration rates on the mixed traffic flow-density basic diagram is obtained. The analysis results are as follows: As the CAV penetration rate continues to increase, the maximum traffic volume and blocking density that a single road can meet are also gradually increasing. In particular, when the CAV penetration rate reaches 100%, the flow peak is reached at a relatively high density, and the flow peak reaches 4500veh / h / ln, which is much higher than other situations. This shows that CAV can increase flow at lower density and maintain higher flow at higher density, reflecting the advantages of CAV in dealing with high-density traffic conditions.

[0117] like Figure 4 As shown in the figure, a parameter sensitivity analysis of bus penetration is performed, with the free flow speed set to 80 km / h, CAV penetration rate to 0.5, CAV queue intensity to 0, and P b Taking the values ​​as 0, 0.05, 0.1, and 0.15 respectively, the influence of different CAV penetration rates on the basic diagram of mixed traffic flow-density is obtained. The analysis results are as follows: Under different bus penetration rates, there is no obvious difference in the peak road flow of mixed traffic, all between 1750veh / h / ln and 2250veh / h / ln. The road congestion density gradually decreases with the increase of bus content, indicating that the increase of buses has not significantly improved the maximum flow capacity of the system. This may be because buses often occupy more road space, limiting the mobility of other vehicles and thus causing a negative impact on the overall traffic flow.

[0118] like Figure 5 As shown in the figure, a parameter sensitivity analysis of CAV queue intensity is performed. The free flow speed is set to 80 km / h, the CAV penetration rate is 0.5, the bus penetration rate is 0, and O is set to -1, -0.5, 0, 0.5, and 1 respectively. The influence of different CAV queue intensities on the mixed traffic flow-density basic diagram is obtained. The analysis results are as follows: the flow peak gradually increases with the increase of CAV queue intensity. The queue intensity has no effect on the blocking density. Therefore, when the traffic density is high, the curves tend to converge, which means that when the traffic density is high, the influence of CAV queue intensity on the flow gradually weakens. The above analysis shows that when designing traffic control strategies, the reasonable queue intensity of CAV should be considered to optimize road use efficiency and reduce congestion.

[0119] S4. Consider the influence of different factors on the flow stability of mixed traffic flow under the influence of random headway:

[0120] Let T represent the running time of the traffic flow, and the number of vehicles N(T) that can pass in a fixed time T is:

[0121]

[0122] in, Represents the mean of all types of headway times.

[0123] Next, we calculate the mean and variance of mixed traffic flow when T→∞, assuming that each headway h mn have a finite mean μ mn and variance make represents the equivalent headway, then the mean and variance of the total headway h” are:

[0124]

[0125] According to the central limit theorem, when N→∞, we have:

[0126]

[0127] Therefore, when T→∞, the mean and variance of the asymptotic normal distribution of N(T) are:

[0128]

[0129] Because q represents the number of vehicles passing through the road section per unit time, N(T) and q obey the relationship:

[0130]

[0131] Correspondingly, the mean and variance of the asymptotic normal distribution that q obeys are:

[0132]

[0133] Based on the above derived expression, the mixed traffic flow stability discriminant diagram is drawn under different CAV penetration rates, different bus penetration rates and different CAV queuing intensity vehicles. It is worth noting that it is assumed here that each type of headway obeys a normal distribution and has different variances and means. The specific settings are shown in Table 2 below.

[0134] Table 2

[0135]

[0136] like Figure 6 As shown in Figure 2, the basic diagram of different CAV penetration rates is studied, and the solid line is the average flow rate calculated through the above analysis. c The value of is set to 0, 0.5, 1, the duration T is 1h, and the queue intensity O is 0, that is, all vehicles are randomly distributed. From the figure, we can conclude that: c The larger the value of , the smaller the scatter of mixed traffic flow. c=1.0, at this time all vehicles are CAVs, the scattering of mixed traffic flow is minimal, that is, the stability of the flow is better. This is consistent with the above theoretical analysis, because the average value of the CAV headway is set smaller and the variance is minimized, which means that the larger P1 is, the higher the stability of the road flow.

[0137] like Figure 7 As shown in Figure 2, the basic diagram under different bus penetration rates is studied, and the solid line is the average flow rate calculated through the above analysis. b The value of is set to 0, 0.05, and 0.1, and the duration T is set to 1h, and the queue intensity O is set to 0, that is, all vehicles are randomly distributed. From the figure, we can conclude that: b The larger the value of , the smaller the scatter of mixed traffic flow. This is because the headway of buses is given a larger mean and variance in the setting, and an increase in the proportion of buses on the road will lead to less road capacity and increase the instability of road flow.

[0138] like Figure 8 As shown in the figure, the basic diagram of different CAV queue intensities is studied. The solid line is the average flow rate calculated through the above analysis. The value of O is set to -1, 0, 1, and the duration T is 1h. c The value of P is 0.5, b The value of is 0. From the figure, we can conclude that when the value of O is 1, the scattering of the corresponding mixed traffic flow is the largest, that is, the road flow is the most unstable. This is because when CAV vehicles are lined up in a row, it will also increase the formation of buses following buses with a large headway, which will lead to unstable road flow. From this perspective, deploying dedicated lanes exclusively for CAVs may be a good solution to improve the efficiency of the traffic system while reducing the randomness of traffic flow.

[0139] Example 3

[0140] The third embodiment of the present invention provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the basic graph model analysis method for urban mixed traffic flow as described above is implemented. The method includes: selecting different following models according to the type of following vehicle, and setting nine headway types with different front and rear vehicle types; calculating the transition probability corresponding to each following mode in the urban mixed traffic flow based on the Markov chain model; deriving the basic graph model of the mixed traffic flow under a single lane based on the transition probability, considering the impact of the penetration rate of connected autonomous driving vehicles, the penetration rate of buses and the queuing intensity of connected autonomous driving vehicles on the road capacity; considering the influence of random headway, analyzing the impact of different influencing factors on the flow stability of the mixed traffic flow.

[0141] Example 4

[0142] Embodiment 4 provides a computer device, comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the above-mentioned urban mixed traffic flow basic graph model analysis method, the method comprising: selecting different following models according to the type of following vehicles, and setting nine headway types with different front and rear vehicle types; calculating the transition probability corresponding to each following mode in the urban mixed traffic flow based on the Markov chain model; deriving the basic graph model of the mixed traffic flow under a single lane based on the transition probability, considering the impact of the penetration rate of connected autonomous driving vehicles, the penetration rate of buses and the queuing intensity of connected autonomous driving vehicles on the road capacity; considering the impact of different influencing factors on the flow stability of the mixed traffic flow under the influence of random headway.

[0143] Example 5

[0144] This embodiment 5 provides an electronic device, including: a processor, a memory and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the basic graph model analysis method for urban mixed traffic flow as described above, the method comprising: selecting different following models according to the type of following vehicle, and setting nine headway types with different front and rear vehicle types; based on the Markov chain model, calculating the transition probability corresponding to each following mode in the urban mixed traffic flow; based on the transition probability, deriving the basic graph model of mixed traffic flow under a single lane, considering the impact of the penetration rate of connected autonomous driving vehicles, the penetration rate of buses and the queuing intensity of connected autonomous driving vehicles on road capacity; considering the impact of different influencing factors on the flow stability of mixed traffic flow under the influence of random headway.

[0145] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take 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.) containing computer-usable program code.

[0146] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0149] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative work on the basis of the technical solution disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A basic graph model analysis method for urban mixed traffic flow in a connected vehicle environment, characterized in that: include: Select different following models according to the type of vehicle being followed, and set nine different headway distance types for different types of vehicles in front and behind; Based on the Markov chain model, the transition probability corresponding to each following mode in urban mixed traffic flow is calculated; Based on the transition probability, the basic graph model of mixed traffic flow under a single lane is derived, considering the impact of the penetration rate of connected autonomous vehicles, the penetration rate of buses, and the intensity of the queues of connected autonomous vehicles on the road capacity; Considering the influence of random headway, the influence of different influencing factors on the flow stability of mixed traffic flow is analyzed.

2. The basic graph model analysis method for urban mixed traffic flow in a connected vehicle environment according to claim 1 is characterized in that: According to the different types of vehicles in front and behind, the headway is divided into the following nine types: 1) h cc Indicates the headway time between a CAV and another CAV; 2)h ch Indicates the headway time between the CAV and the HV; 3)h cb Indicates the headway time of the CAV following the bus; 4)h hc Indicates the headway time of the HV following the CAV; 5)h hh Indicates the headway time of an HV following another HV; 6)h hb Indicates the headway time of the HV following the bus; 7)h bc Indicates the headway time of the bus following the CAV; 8)h bh Indicates the headway time of the bus following the HV; 9)h bb Indicates the headway time when a bus is following another bus.

3. The basic graph model analysis method for urban mixed traffic flow in a connected vehicle environment according to claim 2 is characterized in that: The following models corresponding to different following vehicle types are: 1) When the following vehicle is an intelligent connected vehicle, the corresponding following model is: n =l+h C v n ; Among them, s n is the distance between the front of the nth vehicle and the rear bumper of the preceding vehicle, l is the length of the nth vehicle, v n is the speed of the nth vehicle, h C is the time distance between the nth vehicle and the rear bumper of the preceding vehicle. 2) When the following vehicle is a human-driven vehicle, the corresponding following model is: s n =l+s0+v n h H ; where l and v n They refer to the length and speed of the nth HV vehicle, s0 refers to the minimum safe distance that the nth vehicle should maintain with the vehicle in front, and h H Refers to the constant time distance between the nth vehicle and the rear bumper of the preceding vehicle. 3) When the following vehicle is a bus, the corresponding following model is: s n =l'+s1+v n h B ; where l' and v n They refer to the length and speed of the nth bus, s1 refers to the minimum safe distance that the nth bus should maintain with the preceding bus, and h B Refers to the set constant time distance between the nth vehicle and the rear bumper of the vehicle in front.

4. The basic graph model analysis method for urban mixed traffic flow in a connected vehicle environment according to claim 1 is characterized in that: The specific calculation steps based on the Markov chain model are as follows: define the state space of the discrete Markov chain used to describe the headway transfer as S = {B, H, C}, where B represents a bus, H represents an HV, and C represents a CAV; define the form of the transfer matrix T of the discrete Markov chain used to describe the headway transfer; and define the transfer probability values ​​of each item in the transfer matrix T of the discrete Markov chain according to the characteristics of the mixed traffic flow.

5. The basic graph model analysis method for urban mixed traffic flow in a connected vehicle environment according to claim 1 is characterized in that: Based on the transition probability, the basic graph model of mixed traffic flow in a single lane is derived, considering the impact of the penetration rate of connected autonomous vehicles, the penetration rate of buses, and the queuing intensity of connected autonomous vehicles on road capacity, including: Under urban mixed traffic flow, set h mn (m,n∈S) is the expected headway of nine different types of headways. The total headway of mixed traffic flow is calculated to obtain the density of a single lane. The road operation state is divided into a free flow state and a congested flow state, and the maximum capacity function of a single lane is calculated.

6. The basic graph model analysis method for urban mixed traffic flow in a connected vehicle environment according to claim 1 is characterized in that: Consider the impact of random headway on traffic flow, including: let T represent the running time of traffic flow, determine the number of vehicles that can pass in a fixed time T; calculate the mean and variance of mixed traffic flow when T→∞, assuming that each headway h mn have a finite mean μ mn and variance Determine the mean and variance of the total headway h", determine the mean and variance of the asymptotic normal distribution obeyed by the number of vehicles that can pass in a fixed time T, and determine the mean and variance of the asymptotic normal distribution obeyed by the number of vehicles passing through the road section per unit time.

7. A basic graph model analysis system for urban mixed traffic flow in a vehicle networking environment, characterized in that: include: A selection and determination module is used to select different following models according to the type of the following vehicle, and to set nine headway types with different types of front and rear vehicles; A calculation module is used to calculate the transfer probability corresponding to each following mode in the urban mixed traffic flow based on the Markov chain model; The derivation and determination module is used to derive the basic graph model of mixed traffic flow under a single lane based on the transition probability, and determine the impact of the penetration rate of connected autonomous vehicles, the penetration rate of buses, and the intensity of the queues of connected autonomous vehicles on the road capacity; The analysis module is used to analyze the impact of different influencing factors on the flow stability of mixed traffic flow under the influence of random headway.

8. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the basic graph model analysis method for urban mixed traffic flow in a vehicle networking environment as described in any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the basic graph model analysis method of urban mixed traffic flow in a vehicle network environment as described in any one of claims 1-6.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the basic graph model analysis method for urban mixed traffic flow in a vehicle networking environment as described in any one of claims 1-6.

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