Quantitative calculation method for orderliness of passenger and freight mixed heterogeneous traffic flow under intelligent network environment

Abstract

The application provides a kind of passenger and freight mixed heterogeneous traffic flow order quantitative calculation method under intelligent network connection environment, and relates to the traffic flow order quantitative calculation method of traditional human-driven vehicle (Human-driving Vehicle, HV) and connected and automated vehicles (Connected and Automated Vehicles, CAV) in passenger and freight mixed heterogeneous traffic flow scene under intelligent network connection environment.Three different vehicle following states of mixed state, random state and separation state exist in intelligent network connection traffic flow, and different types of vehicle following state under the same penetration rate will significantly affect road capacity.The application defines the order of passenger and freight mixed heterogeneous traffic flow under intelligent network connection environment by calculating the probability of different vehicle following states and analyzing the capacity, and calculates the relative entropy by using KL divergence, so as to quantitatively measure the order of passenger and freight mixed heterogeneous traffic flow under intelligent network connection environment, and provide a theoretical basis for intelligent network connection road traffic capacity control.

Description

Technical Field

[0001] This invention belongs to the technical field of traffic capacity management for mixed heterogeneous passenger and freight traffic under intelligent connected environments. It relates to a quantitative calculation method for the orderliness of mixed heterogeneous passenger and freight traffic flow under intelligent connected environments, specifically a quantitative calculation method for the orderliness of traffic flow in mixed heterogeneous traffic flow scenarios of CAV buses, CAV freight cars, HV buses, and HV freight cars under intelligent connected environments. Background Technology

[0002] With the deep integration of emerging technologies such as cloud computing, big data, mobile internet, the Internet of Things, and artificial intelligence with transportation systems, emerging intelligent transportation systems centered on autonomous driving technology will provide drivers with intelligent and connected travel services, vehicle-road cooperation, and autonomous driving. Because the widespread adoption of CAVs (Continuing Access Vehicles) is a long-term process, future roads will long contain mixed and heterogeneous traffic flows consisting of CAV buses, CAV freight cars, HV buses, and HV freight cars. How to manage traffic operations in this complex and heterogeneous traffic flow environment composed of multiple vehicle types, especially quantifying the orderly flow of mixed passenger and freight traffic, has forward-looking theoretical research value and practical application significance for improving road capacity.

[0003] The inventors have already theoretically derived and simulated the quantitative calculation method of the orderliness of heterogeneous traffic flow in intelligent connected environments in the paper "Hybrid Characteristics of Heterogeneous Traffic Flow in Intelligent Connected Systems" (Journal of Southwest Jiaotong University, 2022, 57(04): 761-768). However, existing research only focuses on heterogeneous traffic flows composed of passenger vehicles. Considering that freight vehicles are mixed in the traffic flow, it is necessary to redefine and expand the orderliness of mixed passenger and freight heterogeneous traffic flows to be closer to the actual situation. This will help to further understand the evolution law of complex mixed passenger and freight heterogeneous traffic flows in intelligent connected environments and provide a better theoretical reference for effectively improving road capacity. Summary of the Invention

[0004] To better align with actual conditions and reveal the evolutionary patterns of mixed passenger and freight traffic flow in an intelligent connected environment, this invention innovatively improves upon existing research and proposes a quantitative calculation method for the orderliness of mixed passenger and freight traffic flow in an intelligent connected environment.

[0005] This invention relates to a method for quantitatively calculating the orderliness of traffic flow in a mixed passenger and freight heterogeneous traffic flow scenario under intelligent connected vehicle (ICV) conditions, involving both connected and automated vehicles (CAVs) and traditional human-driving vehicles (HVs). Intelligent connected traffic flow exhibits three different vehicle following states: mixed, random, and separated. Under the same penetration rate, different vehicle following states significantly affect road capacity. This invention defines the orderliness of mixed passenger and freight heterogeneous traffic flow under intelligent connected vehicle conditions by calculating the probability of different vehicle following states and analyzing capacity. It also utilizes KL divergence to calculate relative entropy, thereby quantitatively measuring the orderliness of mixed passenger and freight heterogeneous traffic flow under intelligent connected vehicle conditions and providing a theoretical basis for traffic capacity management on intelligent connected roads.

[0006] The specific technical solution adopted by this invention to solve its technical problem is as follows:

[0007] A quantitative calculation method for the orderliness of mixed heterogeneous passenger and freight traffic flow in an intelligent connected environment is characterized by: defining the orderliness of mixed heterogeneous passenger and freight traffic flow in an intelligent connected environment by calculating the probability of different vehicle following states and analyzing traffic capacity, and using KL divergence to calculate relative entropy, thereby quantitatively measuring the orderliness of mixed heterogeneous passenger and freight traffic flow in an intelligent connected environment.

[0008] Furthermore, it includes the following steps:

[0009] Step 1: Define three different vehicle following states for mixed heterogeneous traffic flow of passengers and freight in an intelligent connected environment: mixed state, random state, and separated state.

[0010] Step 2: Define the orderliness of vehicle following status in a mixed passenger and freight heterogeneous traffic flow under an intelligent connected environment;

[0011] Step 3: Based on Markov chain theory, calculate the probability of 16 different car-following vehicle states in the traffic flow by pairwise combinations of four types of vehicles: CAV buses, CAV trucks, HV buses, and HV trucks.

[0012] Step 4: Calculate the relative entropy using KL divergence to quantitatively measure the orderliness of heterogeneous passenger and freight traffic flow in an intelligent connected environment.

[0013] Among them, the maximum traffic capacity of three different vehicle following states in the intelligent connected environment—hybrid state, random state, and separate state—is significantly different.

[0014] Furthermore, in a traffic flow under an intelligent connected environment, when CAV buses follow CAV buses, CAV trucks follow CAV trucks, HV buses follow HV buses, and HV trucks follow HV trucks, and when CAV vehicles and HV vehicles are arranged in an orderly manner, it is defined as orderliness.

[0015] KL divergence is used to quantitatively calculate relative entropy, which quantitatively describes the difference in probability distributions of following states of two different vehicles, and measures the orderliness of heterogeneous passenger and freight traffic flows.

[0016] Furthermore, in step 1, the mixed heterogeneous traffic flow of passengers and freight in the intelligent connected environment has three different vehicle following states: mixed state, random state, and separated state. According to the numerical simulation results, under the same CAV vehicle penetration rate and passenger car ratio, the maximum capacity of the road in the separated state of vehicle following is significantly greater than the maximum capacity of the road in the mixed state and random state of vehicle following.

[0017] Furthermore, in step 2, the orderly definition of mixed heterogeneous traffic flow of passengers and freight vehicles in the intelligent connected environment is: the following state of all vehicles on the road is CAV passenger vehicle-CAV passenger vehicle, CAV freight vehicle-CAV freight vehicle, HV passenger vehicle-HV passenger vehicle, HV freight vehicle-HV freight vehicle, and CAV vehicles and HV vehicles follow each other in an orderly manner; therefore, the more orderly the following states of CAV vehicles and HV vehicles appear in the traffic flow, the closer the traffic flow is to an orderly state.

[0018] Furthermore, in step 3, the mixed heterogeneous traffic flow contains four types of vehicles: CAV buses, CAV freight cars, HV buses, and HV freight cars. Their following states on the road are: CAV bus-CAV bus, CAV bus-HV bus, CAV bus-CAV freight car, CAV bus-HV freight car, HV bus-HV bus, HV bus-HV freight car, HV bus-CAV bus, HV bus-CAV freight car, HV freight car-CAV bus, HV freight car-CAV freight car, HV freight car-HV bus, HV freight car-HV freight car, CAV freight car-CAV freight car, CAV freight car-HV freight car, CAV freight car-CAV bus, and CAV freight car-HV bus, totaling 16 types.

[0019] Definition 1: Assuming the CAV vehicle penetration rate in traffic flow is p, then the HV vehicle penetration rate is 1-p. Since vehicles include passenger cars and freight cars, assuming the proportion of passenger cars in traffic flow is q, then the proportion of freight cars is 1-q.

[0020] Definition 2: Assume that the probability of a CAV passenger car appearing in the traffic flow is P1, the probability of a CAV freight car appearing is P2, the probability of a HV passenger car appearing is P3, and the probability of a HV freight car appearing is P4; From Definition 1, we know that P1 = p·q, P2 = p·(1-q), P3 = (1-p)·q, P4 = (1-p)·(1-q);

[0021] According to Definitions 1 and 2, the probability of a CAV bus-CAV following state occurring in traffic flow is P1. 2 The probability of a CAV bus following a CAV truck is P1P2; the probability of a CAV bus following an HV bus is P1P3; the probability of a CAV bus following an HV truck is P1P4; and the probability of an HV bus following another HV bus is P3. 2 The probability of an HV bus following an HV truck is P3P4; the probability of an HV bus following a CAV bus is P3P1; and the probability of an HV bus following a CAV truck is P3P2. The probability of an HV truck following a CAV bus is P4P1; the probability of an HV truck following a CAV truck is P4P2; the probability of an HV truck following an HV bus is P4P3; and the probability of an HV truck following another HV truck is P4. 2 The probability of a CAV truck following another CAV truck is P2. 2 The probability of a CAV truck following an HV truck is P2P4, the probability of a CAV truck following a CAV bus is P2P1, and the probability of a CAV truck following an HV bus is P2P3.

[0022] Furthermore, in step 4, the relative entropy is calculated using KL divergence to quantitatively describe the orderliness of the mixed heterogeneous traffic flow. The quantitative relationship for measuring the orderliness of the mixed heterogeneous traffic flow is as follows:

[0023] D KL (U||V)=-log(P1 2 +P2 2 ) = -logp 2 (1-2q+2q 2 Equation (1), (0 < q < 1)

[0024] In the formula, D KL Let KL be the divergence, U and V represent the probability distributions of the orderly and following states of traffic flow, respectively, p is the penetration rate of CAV vehicles in the traffic flow, and q is the penetration rate of passenger vehicles in the traffic flow.

[0025] From equation (1), we know that when the proportion of passenger cars q is constant, as the CAV penetration rate p increases, the relative entropy of the mixed heterogeneous flow decreases, the road traffic flow tends to be more orderly, and more CAV vehicles travel in CACC queues. When the CAV market penetration rate p is constant, as the proportion of passenger cars q increases, the relative entropy of the mixed heterogeneous flow first increases and then decreases. When the proportion of passenger cars is 0.5, the relative entropy of the heterogeneous flow is the largest, and the road traffic flow is the most disordered. When the proportion of passenger cars is close to 0 or 1, the relative entropy decreases, the road traffic flow tends to be more orderly, and more CAV vehicles travel in CACC queues.

[0026] Furthermore, in an intelligent connected environment, under the same traffic flow density and CAV penetration rate, as well as the same passenger vehicle penetration rate, three typical car-following states may occur in the traffic flow: mixed state, random state, and separated state. In the mixed state, CAV buses, HV buses, CAV trucks, and HV trucks are arranged in a regular interval, that is, CAV vehicles and HV vehicles are arranged in a regular interval. In the random state, CAV buses, HV buses, CAV trucks, and HV trucks are randomly arranged. In the separated state, CAV vehicles and HV vehicles are separated, with CAV buses following CAV buses, CAV trucks following CAV trucks, HV buses following HV buses, and HV trucks following HV trucks, and each in an orderly order.

[0027] Compared to existing technologies, this invention and its preferred solutions define the orderliness of mixed passenger and freight heterogeneous traffic flow in an intelligent connected environment by calculating the probability of different vehicle following states and analyzing traffic capacity. They also use KL divergence to calculate relative entropy, thereby quantitatively measuring the orderliness of mixed passenger and freight heterogeneous traffic flow in an intelligent connected environment and providing a theoretical basis for traffic capacity management of intelligent connected roads. Attached Figure Description

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0029] Figure 1 This is a sequence diagram of three typical car-following states of intelligent connected passenger and freight mixed heterogeneous traffic flow in an embodiment of the present invention;

[0030] Figure 2 This is a comparison chart of the traffic capacity of different traffic flows under the same CAV penetration rate in the car-following state according to an embodiment of the present invention;

[0031] Figure 3 This is a graph showing the relationship between CAV vehicle penetration rate and relative entropy in an embodiment of the present invention. Detailed Implementation

[0032] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0033] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0034] This invention considers three typical car-following states that may occur in traffic flow under the same traffic flow density, CAV penetration rate, and passenger vehicle penetration rate in an intelligent connected environment: mixed state, random state, and separated state. In the mixed state, CAV buses, HV buses, CAV trucks, and HV trucks are arranged in a regular, alternating order; that is, CAV vehicles and HV vehicles are arranged in a regular, alternating order. In the random state, CAV buses, HV buses, CAV trucks, and HV trucks are randomly arranged. In the separated state, CAV vehicles and HV vehicles are separated, with CAV buses following CAV buses, CAV trucks following CAV trucks, HV buses following HV buses, and HV trucks following HV trucks, and each in an ordered order. Figure 1 As shown.

[0035] At the same CAV penetration rate (0.5) and bus penetration rate (0.2), road capacity will show significant differences under different vehicle following ordering methods, such as... Figure 2 As shown, the maximum capacity of traffic flow in the separated state is significantly higher than that in the random and separated states. The essential factor is the different following states of vehicles in the traffic flow, that is, the difference in the orderliness of vehicle driving sequence in the mixed passenger and freight traffic flow leads to the difference in road capacity.

[0036] To further illustrate the relationship between relative entropy and CAV vehicle penetration rate (bus proportion 0.5), the relative entropy gradually decreases as the CAV vehicle penetration rate increases, such as... Figure 3 As shown.

[0037] To better understand the technical content of this invention, the following mainly describes the derivation process of the following state probability and relative entropy of different types of vehicles in heterogeneous traffic flow with mixed passenger and freight traffic in an intelligent connected environment.

[0038] (1) Probability analysis of car-following states of different types of vehicles in mixed heterogeneous traffic flow of passenger and freight vehicles under intelligent connected environment

[0039] Assume there are N vehicles on a single lane, where 1 represents a CAV (Continuously Operated Vehicle), 2 represents a CAV (Continuously Operated Vehicle), 3 represents a HV (Continuously Operated Vehicle), and 4 represents an HV (Continuously Operated Vehicle). The proportion of CAV vehicles is p, the proportion of HV vehicles is 1-p, the proportion of buses is q, and the proportion of trucks is 1-q. In this Markov chain, A n Let S represent the type of the nth vehicle on the road, with a value space of S:={1,2,3,4}. As N→∞, Pr(Α n=1)=P1 represents the probability that the first vehicle is a CAV bus, Pr(A) n =2)=P2 represents the probability that the first vehicle is a CAV truck, Pr(A) n =3) = P3 represents the probability that the first vehicle is an HV bus, Pr(A) n Let P1 = p·q, P2 = p·(1-q), P3 = (1-p)·q, and P4 = (1-p)·(1-q). The probability space of the type of the nth vehicle on a single lane is π, then π = [P1, P2, P3, P4].

[0040] Define the state transition matrix for the vehicle type:

[0041]

[0042] t sr =Pr(Α n+1 =r|Α n =s), This represents the probability that the (n+1)th car is car r, given that the nth car is car s. Under a random distribution, the type of the (n+1)th car is unrelated to the type of the nth car, i.e., t 41 =t 31 =t 21 =t 11 =P1,t 42 =t 32 =t 22 =t 12 =P2,t 43 =t 33 =t 23 =t 13 =P3,t 44 =t 34 =t 24 =t 14 =Ρ4.

[0043] Define car-following behavior: F 11 Representing CAV buses and following the lead of CAV buses, F 12 This represents a CAV (caravan) following a CAV (bus), and so on. The value space for the following behavior is defined as S. E :

[0044] S E =[F 11 ,F 12 ,F 13 ,F 14 ,F 21 ,F 22 ,F 23 ,F 24 ,F 31 ,F32 ,F 33 ,F 34 ,F 41 ,F 42 ,F 43 ,F 44 Equation (3)

[0045] Let X be a random variable representing the car-following behavior of a vehicle. i For the following behavior of the i-th vehicle, then x i ∈S E Then the probability space of the car-following behavior is π. E ,but:

[0046] π E =[P1t 11 ,P1t 12 ,P1t 13 ,P1t 14 ,P2t 21 ,P2t 22 ,P2t 23 ,P2t 24 ,P3t 31 ,P3t 32 ,P3t 33 ,P3t 34 ,P4t 41 ,P4t 42 ,P4t 43 ,P4t 44 ]

[0047] =[P1 2 P1P2, P1P3, P1P4, P2P1, P2 2 P2P3, P2P4, P3P1, P3P2, P3 2 P3P4, P4P1, P4P2, P4P3, P4 2 ]

[0048] Define f 1223 For the previous step, x n It is F 12 Under the condition that, the next car-following action is x n+1 It is F 23 The probability of following the carousel can be used to obtain the state transition matrix for the carousel behavior:

[0049]

[0050] again

[0051]

[0052] Carry state x n+1 =F12 With x n =F 11 Mutually exclusive, then f 1211 =0.

[0053] After simplification, we get:

[0054]

[0055]

[0056] The probability of a CAV bus following another CAV bus on the road is P1. 2 The probability of a CAV bus following a CAV truck is P1P2; the probability of a CAV bus following an HV bus is P1P3; the probability of a CAV bus following an HV truck is P1P4; and the probability of an HV bus following another HV bus is P3. 2 The probabilities of HV bus-HV truck following each other are P3P4; the probabilities of HV bus-CAV bus following each other are P3P1; the probabilities of HV bus-CAV truck following each other are P3P2; the probabilities of HV truck-CAV bus following each other are P4P1; the probabilities of HV truck-CAV truck following each other are P4P2; the probabilities of HV truck-HV bus following each other are P4P3; and the probabilities of HV truck-HV truck following each other are P4. 2 The probability of a CAV truck following another CAV truck is P². 2 The probability of a CAV truck following an HV truck is P2P4, the probability of a CAV truck following a CAV bus is P2P1, and the probability of a CAV truck following an HV bus is P2P3.

[0057] (2) Quantitative analysis of the orderliness of mixed heterogeneous passenger and freight traffic flow in intelligent connected environment

[0058] Entropy is essentially the "inherent degree of disorder" of a system. The car-following arrangement of CAV buses, CAV trucks, HV buses, and HV trucks in a mixed passenger-freight heterogeneous traffic flow can be considered as the "inherent degree of disorder" of a road system. Relative entropy (KL divergence) describes the degree of disorder between two probability distributions U(x... i ) and V(x i One method to differentiate is shown in equation (5). For ease of writing, the base of all log functions below is assumed to be 2.

[0059]

[0060] Suppose that the catastrophe state of the ordered flow follows a probability distribution U(x) i The car-following state of a real mixed passenger and freight heterogeneous flow follows a probability distribution V(x) i), then U(x i ) and V(x i Let P be two probability distributions on the car-following state random variable X. In an intelligent network environment, the car-following state of CAV vehicles on the road can be acquired in real time, and the sum of the frequencies of CAV bus-CAV bus and CAV truck-CAV truck is obtained as P. C Then we have:

[0061]

[0062]

[0063]

[0064] In a random state, the frequency P of the CAV bus-CAV bus and CAV truck-CAV truck following states is... C =P1 2 P T =P2 2 Then the relative entropy of the random state is:

[0065] D KL (U||V)=-log(P1 2 +P2 2 ) = -logp 2 (1-2q+2q 2 Equation (9) (0 < q < 1)

[0066] When the passenger vehicle penetration rate q = 0, the relative entropy of the random state in the traffic flow, which consists only of freight vehicles, degenerates to:

[0067] D KL (U||V)=-log(P2 2 Equation (10) = -2logp

[0068] When the passenger vehicle penetration rate q = 1, that is, when there are only passenger vehicles in the traffic flow, the relative entropy of the random state degenerates to:

[0069] D KL (U||V)=-log(P1 2 Equation (11) = -2logp

[0070] When there are only passenger cars or freight cars in the traffic flow, the car-following arrangement of vehicles in the traffic flow evolves into a car-following state of the same type of vehicles, so the formula for calculating relative entropy is the same. When there are only passenger cars in the traffic flow, the derivation result of the relative entropy of the random state is consistent with the result in "Mixed Characteristics of Intelligent Connected Heterogeneous Traffic Flow" (Journal of Southwest Jiaotong University, 2022, 57(04): 761-768), which proves the correctness of the derivation of the relative entropy formula under mixed passenger and freight heterogeneous traffic flow.

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0072] This patent is not limited to the above-described preferred embodiments. Anyone can derive other methods for quantitatively calculating the orderliness of heterogeneous passenger and freight traffic flow in an intelligent connected environment based on the inspiration of this patent. All equivalent changes and modifications made within the scope of this patent application shall fall within the scope of this patent.

Claims

1. A method for quantitatively calculating the orderliness of a passenger and freight mixed heterogeneous traffic flow in an intelligent network environment, characterized in that: The order of the passenger and freight mixed heterogeneous traffic flow under the intelligent connected environment is defined by calculating the probability of different vehicle following states and analyzing the traffic capacity, and the relative entropy is calculated by using the KL divergence, so as to quantitatively measure the order of the passenger and freight mixed heterogeneous traffic flow under the intelligent connected environment; The method comprises the following steps: Step 1: defining three different vehicle following states of the passenger and freight mixed heterogeneous traffic flow under the intelligent connected environment, namely the mixed state, the random state and the separated state; Step 2: defining the order of the vehicle following state of the passenger and freight mixed heterogeneous traffic flow under the intelligent connected environment; Step 3: calculating the probability of 16 different following types of vehicle states of the CAV passenger car, the CAV freight car, the HV passenger car and the HV freight car in the traffic flow based on the Markov chain theory; Step 4: quantitatively measuring the order of the passenger and freight mixed heterogeneous traffic flow under the intelligent connected environment by calculating the relative entropy by using the KL divergence; In step 2, the order of the passenger and freight mixed heterogeneous traffic flow under the intelligent connected environment is defined as: the following states of all vehicles on the road are CAV passenger car-CAV passenger car, CAV freight car-CAV freight car, HV passenger car-HV passenger car and HV freight car-HV freight car, and the CAV vehicles and the HV vehicles follow in order; In step 3, the four types of vehicles in the mixed heterogeneous traffic flow, namely the CAV passenger car, the CAV freight car, the HV passenger car and the HV freight car, have 16 following states on the road, including CAV passenger car-CAV passenger car, CAV passenger car-HV passenger car, CAV passenger car-CAV freight car, CAV passenger car-HV freight car, HV passenger car-HV passenger car, HV passenger car-HV freight car, HV passenger car-CAV passenger car, HV passenger car-CAV freight car, HV freight car-CAV passenger car, HV freight car-CAV freight car, HV freight car-HV passenger car, HV freight car-HV freight car, CAV freight car-CAV freight car, CAV freight car-HV passenger car, CAV freight car-CAV passenger car and CAV freight car-HV passenger car; Definition 1: Let the CAV vehicle penetration rate in the traffic flow be Then the HV vehicle penetration rate is Since the vehicles include passenger cars and trucks, let the passenger car proportion in the traffic flow be Then the truck proportion is ; Definition 2: Let the probability of a CAV passenger vehicle appearing in the traffic stream be , the probability of a CAV truck appearing be , the probability of an HV passenger vehicle appearing be , and the probability of an HV truck appearing be ; from Definition 1 we have , , , ; According to Definition 1 and Definition 2, the probability of CAV passenger car-CAV passenger car following state in traffic flow is , the probability of CAV passenger car-CAV truck following state is , the probability of CAV passenger car-HV passenger car following state is , the probability of CAV passenger car-HV truck following state is ; the probability of HV passenger car-HV passenger car following state is , the probability of HV passenger car-HV truck following state is , the probability of HV passenger car-CAV passenger car following state is , the probability of HV passenger car-CAV truck following state is ; the probability of HV truck-CAV passenger car following state is , the probability of HV truck-CAV truck following state is , the probability of HV truck-HV passenger car following state is , the probability of HV truck-HV truck following state is ; the probability of CAV truck-CAV truck following state is , the probability of CAV truck-HV truck following state is , the probability of CAV truck-CAV passenger car following state is , the probability of CAV truck-HV passenger car following state is ; In step 4, the relative entropy is calculated by using the KL divergence to quantitatively describe the order of the mixed heterogeneous traffic flow, and the quantitative relationship of the order of the mixed heterogeneous traffic flow is: Formula (1) wherein, is the KL divergence, and denote the probability distribution of the traffic flow in the orderly state and the following state, respectively, is the CAV vehicle penetration rate in the traffic flow, is the passenger car penetration rate in the traffic flow; From (1), it is known that the proportion of passenger cars increases with the increase of CAV penetration , and the relative entropy of mixed heterogeneous flow decreases, and the road traffic tends to be orderly, and more CAV vehicles travel in CACC queue state; when the CAV market penetration is constant, the increase of the proportion of passenger cars makes the relative entropy of mixed heterogeneous flow first increase and then decrease, and the relative entropy of heterogeneous flow is maximum when the proportion of passenger cars is 0.5, and the road traffic is most disordered, and the relative entropy decreases when the proportion of passenger cars approaches 0 or 1, and the road traffic tends to be orderly, and more CAV vehicles travel in CACC queue state.

2. The method for quantitatively calculating the order of the passenger and freight mixed heterogeneous traffic flow under the intelligent connected environment according to claim 1, wherein: The order is defined as the order of the traffic flow under the intelligent connected environment when the CAV passenger car follows the CAV passenger car, the CAV freight car follows the CAV freight car, the HV passenger car follows the HV passenger car and the HV freight car follows the HV freight car, and the CAV vehicles and the HV vehicles follow in order; The relative entropy is quantitatively calculated by using the KL divergence, that is, the difference between the probability distributions of two different vehicle following states is quantitatively described, and the order of the passenger and freight mixed heterogeneous traffic flow is measured.

3. The method for quantitatively calculating the order of the passenger and freight mixed heterogeneous traffic flow under the intelligent connected environment according to claim 1, wherein: Under the intelligent network environment, in the same traffic flow density and CAV penetration rate, and the same passenger car penetration rate, three typical following driving states of traffic flow are allowed to appear, namely mixed state, random state and separated state. In the mixed state, CAV passenger cars, HV passenger cars, CAV trucks and HV trucks are regularly and regularly sorted, that is, CAV vehicles and HV vehicles are regularly and regularly sorted. In the random state, CAV passenger cars, HV passenger cars, CAV trucks and HV trucks are randomly sorted. In the separated state, CAV vehicles and HV vehicles are in a separated state, CAV passenger cars follow CAV passenger cars, CAV trucks follow CAV trucks, HV passenger cars follow HV passenger cars, and HV trucks follow HV trucks, and are sorted in order.

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