Method and system for analyzing basic graph model of urban mixed traffic flow under internet of vehicles environment

Abstract

The application provides a kind of urban mixed traffic flow basic graph model analysis method and system under Internet of Vehicles environment, belongs to urban traffic management and networked automatic driving car application technical field, according to the type of following vehicle, different car-following model is selected, and nine different headway types of front and rear vehicle types are set;Based on Markov chain model, the transfer probability corresponding to each following mode in urban mixed traffic flow is calculated, the basic graph model of mixed traffic flow under single lane is deduced, the influence of networked automatic driving vehicle penetration rate, bus penetration rate and networked automatic driving vehicle queue intensity on road capacity is considered;Under the influence of random headway, the influence of different influencing factors on the flow stability of mixed traffic flow is analyzed.The application discloses the road capacity under different traffic density and vehicle type distribution conditions;Under the existing basic graph model framework, the influence of random headway on traffic flow stability is discussed.

Description

Technical Field

[0001] The present invention relates to the field of urban traffic management and the technical field of connected automated vehicle applications, and in particular to a method and system for analyzing a basic graph model of urban mixed traffic flow that considers the characteristics of intelligent connected vehicles and buses. Background Art

[0002] Connected and Automated Vehicles (CAVs) combine cutting-edge technologies from connected vehicles (CVs) and autonomous vehicles (AVs). They not only assist and automate driving tasks but also enable real-time connectivity with the surrounding environment and other vehicles. This high level of information exchange offers significant advantages in increasing road capacity, enhancing traffic stability, and improving travel convenience. In theory, if all vehicles on the road were CAVs, headway distances would be significantly shortened due to precise coordination between vehicles through real-time communication, resulting in optimal road utilization and traffic efficiency.

[0003] In the evolution of urban transportation systems, many countries have tended to adopt and implement a bus-dominated urban transportation development model. With the continuous development of connected vehicle technology, urban roads will likely experience a mixed traffic landscape of CAVs, HVs, and buses for a considerable period of time. The varying response times resulting from these technological differences may increase traffic instability. In this mixed traffic scenario, significant differences in vehicle response time, operating methods, and headway distances can lead to traffic instability. Therefore, before autonomous driving technology becomes mainstream and widely adopted, how to effectively manage and optimize mixed urban traffic flows has become a pressing issue in transportation research. This requires in-depth analysis of the interaction mechanisms between CAVs, HVs, and buses at different penetration rates, and the design of management and control strategies that can optimize traffic flow in mixed traffic scenarios based on these 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 network 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 that considers the characteristics of intelligent connected vehicles and buses, comprising the following steps:

[0007] S1. Select different following models based on the following vehicle type, and set nine different headway types for different leading and trailing vehicle types;

[0008] S2. Calculate the transition probability corresponding to each following mode in urban mixed traffic flow based on the Markov chain model;

[0009] S3. Based on the transition probabilities from step S2, derive a basic graphical model for mixed traffic flow in a single lane, considering the impact of the penetration rate of connected autonomous vehicles, the penetration rate of buses, and the intensity of connected autonomous vehicle queues on road capacity.

[0010] S4. Considering the influence of random headway, analyze the impact of different factors on the flow stability of mixed traffic flow.

[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 CAV and HV; 3)h cb represents the headway time of the CAV following the bus; 4)h hc Indicates the headway time between HV and CAV; 5)h hh Indicates the headway time between two HVs following each other; 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 follows 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 ; Among them, l and v nrespectively, s0 represents the minimum safety distance that the nth vehicle should keep from the preceding vehicle, h H represents the set constant nth vehicle and the preceding vehicle rear bumper time distance. n n h B ; wherein, l' and v n respectively, s1 represents the minimum safety distance that the nth vehicle should keep from the preceding vehicle, h B represents the set constant nth vehicle and the preceding vehicle rear bumper time distance.

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

[0014] As a further limitation of the first aspect of the application, step S3 is as follows: S3.1, under urban mixed traffic flow, set h mn (m, n ∈ S) as the expected headway of the nine different types of headway, calculate the total headway of the mixed traffic flow, and then obtain the single lane density; S3.2, divide the road running state into free flow state and congestion flow state, and calculate the maximum traffic capacity function of a single lane.

[0015] As a further limitation of the first aspect of the application, 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 through in a fixed time T; calculate the mean and variance of the mixed traffic flow when T→∞, assuming that each headway h mn has 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 that the number of vehicles passing through in a fixed time T obeys, and determine the mean and variance of the asymptotic normal distribution that the number of vehicles passing through the road section per unit time obeys.

[0016] Secondly, the application provides a city mixed traffic flow basic graph model analysis system considering the characteristics of intelligent network connected vehicles and buses, comprising the following steps:

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

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

[0019] A derivation and determination module is used to derive a basic graphical model of mixed traffic flow in a single lane based on transition probabilities, and to determine the impact of the penetration rate of connected autonomous vehicles, bus penetration rate, and the intensity of connected autonomous vehicle queues on 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 for 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 that can be executed 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 network 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 for urban mixed traffic flow in a vehicle network environment as described in the first aspect.

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

[0025] Additional advantages of the present invention will be more clearly given in the following description or learned through 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 following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 This is a flow chart of a method for analyzing a basic graph model of urban mixed traffic flow that considers the characteristics of intelligent connected vehicles and buses, as described in an embodiment of the present invention.

[0028] Figure 2 Schematic diagram of the headway time of various types of vehicles and CAV queue intensity as specified in an embodiment of the present invention.

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

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

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

[0032] Figure 6 Schematic diagram of the sensitivity of different CAV penetration rates to flow stability considering 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 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. 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 having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention.

[0036] Those skilled in the art will understand 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, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," 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 suitable manner in any one or more embodiments or examples. Those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless otherwise contradictory.

[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. However, 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 road sections is studied. On this basis, a method for studying the effect of random headway time on road flow stability 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, a basic graph model analysis system for urban mixed traffic flow in a vehicle network environment is first 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 transition 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 transition probability, and determine 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; an analysis module, which is used to analyze the impact 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-mentioned system, a basic graph model analysis method for urban mixed traffic flow that takes into account the characteristics of intelligent connected vehicles and buses is implemented. The steps include: selecting different following models according to the type of following vehicle, and setting nine different headway types for the front and rear vehicles; 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 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 intensity of queues 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. This embodiment studies the impact of different vehicle penetration rates and queue intensities on road flow in an environment where multiple vehicle types are mixed, as well as the impact of headway randomness on the stability of the basic graph, 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 vehicles in front and behind: 1) h cc Indicates the headway time between a CAV and another CAV; 2)h ch Indicates the headway time between CAV and HV; 3)h cb represents the headway time of the CAV following the bus; 4)h hc Indicates the headway time between HV and CAV; 5)h hh Indicates the headway time between two HVs following each other; 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 follows 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 preceding vehicle.

[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 It represents the probability that the s-type vehicle is followed by the k-type vehicle, that is, 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 follows:

[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, the probability of different following vehicle types is:

[0054]

[0055] When the first vehicle is a CAV, 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 queuing 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 stream density.

[0063] 2) Congestion flow status

[0064]

[0065] Where, 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 impact of random headway on traffic flow is also considered. Let T represent the running time of the traffic flow. The number of vehicles N(T) that can pass in a fixed time T is:

[0067]

[0068] where, represents the mean of all types of headway.

[0069] The mean and variance of the mixed traffic flow when T→∞ are calculated as follows, assuming that each type of headway h mn has a finite mean μ mn and variance Let represent the equivalent headway, then the mean and variance of the total headway h

[0070]

[0071] According to the central limit theorem, when N→∞, there is:

[0072]

[0073] Therefore, when T→∞, the mean and variance of the asymptotic normal distribution that N(T) obeys 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 the capacity model of the road based on the Markov chain, and to perform sensitivity analysis using software such as matlab.

[0080] Embodiment 2

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

[0082] S1, select different car-following models according to the types of following vehicles, and set nine types of headways of different front and rear vehicles:

[0083] According to the different types of front and rear vehicles, the headway is divided into the following nine types, as shown in Figure 2 : 1) h cc represents the headway of CAV following another CAV; 2) h ch represents the headway of CAV following HV; 3) h cbrepresents the headway time of the CAV following the bus; 4)h hc Indicates the headway time between HV and CAV; 5)h hh Indicates the headway time between two HVs following each other; 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 preceding vehicle, which is 2m, and h H Refers to the set constant time distance between the nth vehicle and the rear bumper of the preceding vehicle.

[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 preceding vehicle.

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

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

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

[0097]

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

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

[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, the probability of different following vehicle types is:

[0103]

[0104] When the first vehicle is a CAV, 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 It 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 queuing intensity of CAV vehicles in the traffic flow, with a value range of [-1,1].

[0108] S3. Based on the transition probabilities in step S2, derive a basic graph model for mixed traffic flow in a single lane, considering the impact of the penetration rate of connected autonomous vehicles, the penetration rate of buses, and the intensity of connected autonomous vehicle queues on road capacity:

[0109] 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 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 congested 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 80 km / h. Congested flow state: Where, 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] Based on the basic graph model above, a sensitivity analysis of penetration rate, platoon intensity, and free-flow speed was conducted. It is worth noting that the nine headway times 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 rate is conducted, setting the free flow speed to 80km / h, bus penetration rate to 0, CAV queue intensity to 0, P cTaking values ​​of 0, 0.25, 0.5, 0.75, and 1, we obtain the impact of different CAV penetration rates on the mixed traffic flow-density basic diagram. The analysis results are as follows: As CAV penetration increases, the maximum throughput and congestion density that a single road can accommodate also gradually increase. In particular, when CAV penetration reaches 100%, traffic peaks at relatively high densities, reaching 4500 veh / h / ln, far exceeding other scenarios. This demonstrates that CAVs can increase traffic flow at lower densities and maintain high flow at higher densities, demonstrating their superiority in handling high-density traffic conditions.

[0117] like Figure 4 As shown in the figure, a parameter sensitivity analysis of bus penetration rate is conducted, setting the free flow speed to 80km / h, CAV penetration rate to 0.5, CAV queue intensity to 0, P b Taking the values ​​as 0, 0.05, 0.1, and 0.15 respectively, the impact of different CAV penetration rates on the mixed traffic flow-density basic diagram 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, restricting the mobility of other vehicles and thus having a negative impact on the overall traffic flow.

[0118] like Figure 5 As shown in Figure 2, a parameter sensitivity analysis of CAV queue intensity was conducted. The free-flow speed was set to 80 km / h, the CAV penetration rate was 0.5, the bus penetration rate was 0, and O was varied to -1, -0.5, 0, 0.5, and 1, respectively. The impact of different CAV queue intensities on the mixed traffic flow-density basic diagram was obtained. The analysis results are as follows: peak traffic flow gradually increases with increasing CAV queue intensity. Queuing intensity has no effect on congestion density. Therefore, at high traffic density, the curves converge, indicating that the impact of CAV queue intensity on traffic flow gradually decreases at high traffic density. This analysis indicates that appropriate CAV queue intensity should be considered when designing traffic control strategies to optimize road utilization efficiency and reduce congestion.

[0119] S4. Consider the impact 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. 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.

[0123] Next, we calculate the mean and variance of mixed traffic flow when T→∞, assuming that each headway h mn has 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, a discriminant diagram of mixed traffic flow stability is drawn under different CAV penetration rates, different bus penetration rates, and different CAV queuing intensities. 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 values ​​of are set to 0, 0.5, and 1, and the duration T is 1 hour, and the queue intensity O is 0, that is, all vehicles are randomly distributed. From the figure, we can conclude that: P c The larger the value of , the smaller the scatter of mixed traffic flow. The extreme case is P c= 1.0, all vehicles are CAVs, and the scatter of mixed traffic flow is minimized, which means that the traffic flow is more stable. This is consistent with the theoretical analysis above, because the average value of CAV headway is set to be smaller and the variance is minimized, which means that the larger P1 is, the more stable the road traffic flow is.

[0137] like Figure 7 As shown in Figure 2, the basic diagram of different bus penetration rates is studied, and the solid line is the average flow rate calculated through the above analysis. b The values ​​of are set to 0, 0.05, and 0.1, and the duration T is 1 hour, and the queue intensity O is 0, that is, all vehicles are randomly distributed. From the figure, we can conclude that: P 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. 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 by the above analysis. The value of O is set to -1, 0, 1, and the duration T is 1h. c The value is 0.5, P b The value of is 0. The figure shows that when O is 1, the corresponding mixed traffic flow has the greatest scatter, indicating the most unstable road flow. This is because when CAVs are arranged in a single row, they also increase the number of buses following each other, with large headway intervals, which in turn leads to unstable road flow. From this perspective, deploying dedicated lanes for CAVs may be a good solution to improve traffic system efficiency while reducing the randomness of traffic flow.

[0139] Example 3

[0140] This embodiment 3 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 urban mixed traffic flow basic graph model analysis method as described above is implemented, the method comprising: selecting different following models according to the type of following vehicle, and setting nine different headway types for the 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] This embodiment 4 provides a computer device, including 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 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.

[0143] Example 5

[0144] This embodiment 5 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 to enable the electronic device to execute instructions for implementing 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 vehicle, and setting nine different headway types for the 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.

[0145] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, 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 magnetic 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. 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 that can direct a computer or other programmable data processing device to work 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 The 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 the functions for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0149] Although the above describes the specific embodiments 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 on the basis of the technical solutions disclosed in the present invention without the need for creative work 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 by: include: Select different following models according to the type of vehicle being followed, and set nine different headway types for different types of vehicles in front and behind; including: dividing the headway into the following nine types according to the different types of vehicles in front and behind: h cc Indicates the headway time between a CAV and another CAV; h ch Indicates the headway time between CAV and HV; h cb represents the headway time of the CAV following the bus; h hc Indicates the headway time between HV and CAV; h hh Indicates the headway time between two HVs following each other; h hb Indicates the headway time of the HV following the bus; h bc represents the headway time of the bus following the CAV; h bh Indicates the headway time of the bus following the HV; h bb Indicates the headway time between a bus and another bus; the following models corresponding to different following vehicle types are: When the following vehicle is an intelligent connected vehicle, the corresponding following model is: s 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; when the following vehicle is a human-driven vehicle, the corresponding following model is: s n =l+s0+v n h H ; Among them, 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; when the following vehicle is a bus, the corresponding following model is: s n =l'+s1+v n h B ; Among them, 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 constant time distance between the nth vehicle and the rear bumper of the preceding vehicle; Based on the Markov chain model, the transfer probability corresponding to each following mode in urban mixed traffic flow is calculated; the method includes: defining 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 a HV, and C represents a CAV; defining the form of the transfer matrix T of the discrete Markov chain used to describe the headway transfer; and defining the transfer probability values ​​of each item in the transfer matrix T of the discrete Markov chain based on the characteristics of the mixed traffic flow; 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 intensity of the queues of connected autonomous vehicles on road capacity; including: setting h in mixed traffic flow in the city mn (m,n∈S) is the expected headway of nine different types of headways. The total headway of mixed traffic flow is calculated, and then the density of a single lane is obtained. The road operation state is divided into free flow state and congested flow state, and the maximum capacity function of a single lane is calculated. Considering the influence of random headway, the influence of different factors on the flow stability of mixed traffic flow is analyzed; 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 has 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 the road section per unit time.

2. A basic graph model analysis system for urban mixed traffic flow in a connected vehicle environment, characterized by: include: The selection and determination module is used to select different following models according to the type of vehicle being followed, and to set nine different headway types for different types of vehicles; including: dividing the headway into the following nine types according to the different types of vehicles: h cc Indicates the headway time between a CAV and another CAV; h ch Indicates the headway time between CAV and HV; h cb represents the headway time of the CAV following the bus; h hc Indicates the headway time between HV and CAV; h hh Indicates the headway time between two HVs following each other; h hb Indicates the headway time of the HV following the bus; h bc represents the headway time of the bus following the CAV; h bh Indicates the headway time of the bus following the HV; h bb Indicates the headway time between a bus and another bus; the following models corresponding to different following vehicle types are: When the following vehicle is an intelligent connected vehicle, the corresponding following model is: s 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; when the following vehicle is a human-driven vehicle, the corresponding following model is: s n =l+s0+v n h H ; Among them, 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; when the following vehicle is a bus, the corresponding following model is: s n =l'+s1+v n h B ; Among them, 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 constant time distance between the nth vehicle and the rear bumper of the preceding vehicle; A calculation module is used to calculate the transfer probability corresponding to each vehicle following mode in urban mixed traffic flow based on a Markov chain model. The calculation module includes: defining the state space of a discrete Markov chain used to describe the headway transfer as S = {B, H, C}, where B represents a bus, H represents a HV, and C represents a CAV; defining the form of a transfer matrix T of the discrete Markov chain used to describe the headway transfer; and defining 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. 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; including: setting h under urban mixed traffic flow mn (m,n∈S) is the expected headway of nine different types of headways. The total headway of mixed traffic flow is calculated, and then the density of a single lane is obtained. The road operation state is divided into free flow state and congested flow state, and the maximum capacity function of a single lane is calculated. The analysis module is used to analyze the influence of different factors on the flow stability of mixed traffic flow under the influence of random headway. It includes: 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 has 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 the road section per unit time.

3. 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 the vehicle network environment as claimed in claim 1 is implemented.

4. 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 the vehicle network environment as described in claim 1.

5. An electronic device, characterized in that: include: 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 in a vehicle network environment as described in claim 1.

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