Intelligent rail route time division multiplexing method based on interoperation

Abstract

The invention belongs to the technical field of intelligent transportation, and particularly relates to an interoperation-based intelligent rail route time division multiplexing method, which comprises the following steps that: vehicle data are obtained through roadside equipment, and the vehicle data comprise position coordinates; processing the vehicle data based on a trajectory smoothing method to obtain a fitted vehicle trajectory; processing the plurality of traffic indexes based on a fuzzy evaluation algorithm, and evaluating the traffic state grades according to the weights of the plurality of traffic indexes and the membership degrees of the traffic state grades of the corresponding indexes; when the traffic is in the deadlock state level, the original state is kept for driving; and when the traffic is in a non-locking state, according to the protection time headway, the safety time headway, the controlled network connection vehicle governing range duration and the average driving speed of the vehicles with the priority right of way, the length of the vehicles changing lanes and occupying the intelligent rail and the time of occupying the intelligent rail are obtained.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and specifically relates to a time-division multiplexing method for intelligent rail routes based on interoperability. Background Art

[0002] With the continuous advancement of urbanization and the significant improvement of people's living standards, the number of pedestrians and vehicles on the road has shown a rapid growth trend. Especially in first-tier cities, the number of motor vehicles has increased year by year, which has led to a serious imbalance between traffic supply and demand. Especially during peak hours in the morning and evening, due to the concentrated outbreak of the demand for commuting by a large number of citizens, the existing road system is difficult to effectively cope with this sharp increase in traffic flow in a short period of time, resulting in continuous and serious congestion. Faced with increasingly severe urban traffic challenges, many places have taken a series of measures to try to alleviate this situation, including setting up dedicated bus lanes to ensure the smooth operation of public transportation. However, in actual operation, it was found that these dedicated lanes are often idle during non-peak hours or when the frequency of bus use is low, thereby reducing the lane resources available for other social vehicles, which not only reduces the overall road utilization rate, but sometimes may even aggravate the traffic congestion in local areas.

[0003] In order to resolve the above contradictions and make more efficient use of limited road space, it is particularly important to introduce the concept of flexible right of way. Flexible right of way is a technical means to dynamically adjust the right of different types of vehicles to use specific road sections based on real-time traffic conditions. It aims to optimize the traffic flow efficiency of the entire city by flexibly allocating road resources. As an efficient and environmentally friendly public transportation tool, smart rail plays an important role in many cities. Smart rail dedicated lanes often cause waste of road resources during low-frequency use, especially when traffic demand changes greatly. Therefore, a flexible right of way management and control service system based on vehicle-road collaboration is designed to implement flexible right of way strategies on smart rail dedicated lanes, give full play to the potential of existing infrastructure, and alleviate urban traffic pressure. Summary of the invention

[0004] The present invention is proposed based on the above-mentioned needs of the prior art. The technical problem to be solved by the present invention is to provide an interoperable smart rail route time-sharing multiplexing method to flexibly regulate the controlled vehicles to increase the operating speed and alleviate traffic pressure.

[0005] In order to solve the above problems, the technical solution provided by the present invention includes:

[0006] A time-sharing multiplexing method for intelligent rail routes based on interoperability is provided, comprising: obtaining vehicle data through roadside equipment, the vehicle data including position coordinates; processing the vehicle data based on a trajectory smoothing method to obtain a fitted vehicle trajectory; processing multiple traffic indicators based on a fuzzy evaluation algorithm, and evaluating the traffic state level according to the weights of the multiple traffic indicators and the degree of membership of the corresponding indicators in the traffic state level; when the traffic is in a deadlock state level, maintaining the original state of driving; when the traffic is in a non-lock state, obtaining the length of the vehicle changing lanes and occupying the intelligent rail and the time of occupying the intelligent rail according to the protection headway, the safety headway, the duration of the controlled networked vehicle's dominance range and the average driving speed of vehicles with priority right of way; wherein the protection headway is determined by the safety headway of the corresponding vehicle type and the headway of the controlled vehicle at the corresponding moment obtained according to the ICV following model; the safety headway is determined by the headway of the preceding vehicle and the shortest safety headway; and the duration of the controlled networked vehicle's dominance range is obtained based on the lane changing time.

[0007] Preferably, the vehicle data is processed based on the trajectory smoothing method to obtain the fitted vehicle trajectory, including: obtaining vehicle features in two adjacent frames of data, the vehicle features including the vehicle head angle and the vehicle size; and calculating the similarity of the corresponding vehicles in the two frames of data according to the features, expressed as: Among them, sim represents the similarity of the features between the two vehicles, and d 1 and d 2 Indicates the value of the corresponding vehicle feature in two frames of data; when the similarity exceeds 0.85, it is considered that the corresponding vehicles in the previous and next two frames of data overlap, and the trajectory points of the corresponding vehicle in the latter frame are removed.

[0008] The above settings can be used to eliminate vehicle tracks that are recorded repeatedly, reduce interference and improve accuracy.

[0009] Preferably, the trajectory points are connected, and a straight line y=kx+b is found by the least squares method so that the sum of the vertical distances from each data point to the straight line is minimized, the optimal solution of the trajectory data line is calculated, the vehicle trajectory is fitted, and the slope k of the fitting line of adjacent trajectory points is calculated: In the formula, and are the average values ​​of x and y respectively; calculate the intercept b of the fitting line of adjacent trajectory points: Get the fitted vehicle trajectory.

[0010] Preferably, obtain n samples representing the congestion state, m influencing indicators, and form the original evaluation indicator matrix where x ij represents the jth influencing factor of the i-th sample (i=1,2,…,n; j=1,2,…,m); calculate each index xij The proportion of this factor index p ij : Calculate the entropy value e of the jth indicator j : Where k is determined by the number of samples n, let ln is the natural logarithm, and e j The value should be greater than or equal to 0, and the difference coefficient g of the j-th indicator is calculated. j , the coefficient of variation is expressed as: g j =1-e j , calculate the weight w of each factor index j : w j The set of values ​​is W.

[0011] Preferably, the influence of a single indicator on the road traffic congestion is considered to determine the traffic state level to which it belongs, and the r corresponding to the traffic state level ij is 1, and does not belong to the traffic status level corresponding to r ij Taking the value as 0, we get the combined membership degree R, expressed as:

[0012] Preferably, the comprehensive evaluation matrix B for road congestion is calculated, expressed as: B=W×R, and the level with the maximum membership degree in the comprehensive evaluation matrix for road congestion is selected as the final congestion level to complete the evaluation of the traffic situation.

[0013] Preferably, the protection headway is determined by the safety headway of the corresponding vehicle type and the headway of the controlled vehicle at the corresponding time obtained according to the ICV following model, including: Among them, U1 is the time distance for protecting the head of the vehicle, t safe is the safe headway time of the controlled vehicle of the corresponding type, Δx n (t) is the headway, k 1 is the first coefficient, with a value of 1.0, k 2 is the second coefficient, with a value of 0.1, k 3 is the third coefficient, with a value of 0.58, Δx n (t) is the headway of vehicle n at the tth moment, v n (t) is the speed of vehicle n at the tth moment, v n-1 (t) is the speed of vehicle n-1 at the tth moment, d is the fifth coefficient, which is 5.5, exp is the exponential function, and m is the fourth coefficient, which is 8.83 m / s.

[0014] Preferably, the safe headway is determined by the headway of the preceding vehicle and the shortest safe headway, including: U2 = max(t′ safe,min safe )+1, where U2 is the safe headway, t′ safe is the headway of the preceding vehicle, min safe are the shortest safe time distances, and these two values ​​are specific values ​​regulated by road traffic conditions.

[0015] Preferably, the duration of the controlled connected vehicle's control range is obtained based on the lane change time, including: Con = t change +1, Among them, t change (x|μ,σ) represents the lane-changing time of the vehicle, μ is the mean of the lognormal distribution, σ is the standard deviation of the lognormal distribution, var(X) is the variance, and E(X) represents the mathematical expectation.

[0016] Preferably, the time that the vehicle occupies the smart track when changing lanes is expressed as: T=U1+U2+Con, and the length of the smart track that the vehicle occupies when changing lanes is expressed as: Z=T*v, where v is the average driving speed of high-level vehicles with priority right of way.

[0017] Compared with the prior art, the present invention uses a multi-index fuzzy evaluation algorithm to consider which traffic indicator has a greater impact on the traffic situation under different traffic conditions, and then evaluate the traffic situation based on this, so as to eliminate the interference of other factors and judge the traffic status. In addition, more fine-grained calculations with smaller granularity are performed in places within the scope of the smart track right of way. Through the above-mentioned settings, the controlled vehicle can be driven in different states under different traffic conditions, which can avoid driving the controlled vehicle to aggravate the congestion level of the traffic state level, and can improve the traffic efficiency to a greater extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this specification. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0019] Figure 1 A flowchart of the steps of the interoperable smart rail route time-division multiplexing method provided in this application;

[0020] Figure 2 Schematic diagram of the relationship between indicator data and threshold values ​​in the embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the term "connected" should be understood in a broad sense, for example, it can be a fixed connection, or a detachable connection, or an integral connection, which can be a mechanical connection, or an electrical connection, which can be a direct connection, or can be indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] The terms "top", "bottom", "above", "lower", and "on" used throughout the description are relative to the relative positions of components of a device, such as the relative positions of the top and bottom substrates within a device. It is understood that devices are multifunctional regardless of their orientation in space.

[0024] To facilitate understanding of the embodiments of the present application, further explanation will be given below with reference to specific embodiments in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the embodiments of the present application.

[0025] This embodiment provides a time-division multiplexing method for smart rail routes based on interoperability, such as Figure 1 shown.

[0026] The time-division reuse methods of smart rail routes based on interoperability include:

[0027] Obtain vehicle data through roadside equipment.

[0028] The vehicle data includes the location coordinates of the vehicle.

[0029] For a single vehicle's driving trajectory, coordinate offset and coordinate jitter caused by vehicle detection by roadside equipment are very common and difficult to avoid. This may cause a vehicle to record multiple overlapping trajectory data or redundant data with similar positions at the same time, and there may be a large deviation between the positions of some trajectory points and adjacent trajectory points in the trajectory data. These situations will cause the risk assessment utility to decrease in multiple scenarios. Therefore, adding a trajectory smoothing algorithm must not only adapt to the detection accuracy of sensors of different roadside equipment, but also meet the needs of online processing of large amounts of high-precision data.

[0030] The vehicle data is processed based on the trajectory smoothing method to obtain the fitted vehicle trajectory.

[0031] The trajectory smoothing adopts the spline curve optimization algorithm. First, the continuous data points of the vehicle coordinates (x i ,y i ), set the trajectory data to upload 10 frames of data per unit time. Then reconstruct the data and remove the repeated recorded trajectories. Calculate the similarity of the features (front angle, vehicle size) between the two vehicles:

[0032]

[0033] In the formula, sim represents the similarity of the features between the two vehicles, d 1 and d 2 Indicates the specific values ​​of the features that need to be calculated for the two frames of data. These values ​​are obtained through roadside equipment, and the similarity threshold is set to 0.85. That is, when the similarity exceeds 0.85, the two frames are considered to overlap, and the trajectory point at the latter moment is removed.

[0034] Set the acceptable offset of the current road, connect the vehicle coordinate points sent back by the roadside equipment, and find a straight line y=kx+b through the least squares method, so that the sum of the vertical distances from each data point to this line is minimized, thus calculating the optimal solution of the route of the trajectory data and completing the fitting of the vehicle trajectory. Calculate the slope k of the fitting line of adjacent trajectory points:

[0035]

[0036] In the formula, and are the means of x and y respectively.

[0037] Calculate the intercept b of the fitting line of adjacent trajectory points

[0038]

[0039] Get the fitted vehicle trajectory.

[0040] Based on the fuzzy evaluation algorithm, multiple traffic indicators are processed, and the traffic state level is evaluated according to the weights of multiple traffic indicators and the degree of membership of the corresponding indicators in the traffic state level; when the traffic is in a locked state level, the original state is maintained.

[0041] Flexible right of way is a technical means to dynamically adjust the right of different types of vehicles to use specific road sections based on real-time traffic conditions. It aims to optimize the traffic flow efficiency of the entire city by flexibly allocating road resources. However, when traffic congestion forms a deadlock, the flexible right of way strategy is not applicable. Therefore, it is necessary to first judge the traffic status level.

[0042] In order to improve the judgment of traffic status level, multiple traffic indicators are introduced. Through the fuzzy evaluation algorithm of multiple indicators, it is considered which indicator has a greater impact on the traffic situation under different traffic conditions. The traffic situation is evaluated based on this indicator, so as to accurately obtain the traffic status level.

[0043] Specifically, obtain n samples representing congestion status and m influencing indicators to form the original evaluation indicator matrix where x ij Represents the jth influencing factor of the i-th sample (i=1,2,…,n; j=1,2,…,m).

[0044] Calculate each index x ij The proportion of this factor index p ij :

[0045]

[0046] Calculate the entropy value e of the jth indicator j :

[0047]

[0048] Where k is determined by the number of samples n, let ln is the natural logarithm, and e j The value should be greater than or equal to 0.

[0049] Calculate the coefficient of difference g of the jth indicator j The larger the coefficient of difference, the greater the impact of the index value of the factor on the evaluation result, and vice versa. The coefficient of difference is expressed as:

[0050] g j =1-e j

[0051] Calculate the weight w of each factor indicator j :

[0052]

[0053] Set the threshold of each indicator. Specifically, each indicator has a number Y(x)∈[0,1] corresponding to it. That is, Y is the fuzzy set of the corresponding indicator, and Y(x) is the degree of membership of the indicator to Y. The closer the degree of membership is to 1, the higher the degree to which the indicator belongs to Y. On the contrary, when the degree of membership is closer to 0, the lower the degree to which the indicator belongs to Y. Figure 2As shown in the figure, k1, k2, k3, ..., k10 represent the linear values ​​of a certain indicator near the threshold. According to the position of the actual value of the indicator in the above k value, illustratively, if the value is between k1 and k2, the indicator has two traffic status levels of membership, and if the value is between k2 and k3, the indicator has one traffic status level of membership.

[0054] In a feasible implementation of the embodiment, the indicators include traffic flow density and road saturation. When the traffic congestion level is higher, the values ​​of these two indicators will become larger. Therefore, for Figure 2 For example, the corresponding traffic status levels from left to right are very smooth, smooth, light congestion, moderate congestion, severe congestion, and deadlock. The more traffic status levels there are, the higher the accuracy of judging whether the traffic is deadlocked.

[0055] Determine the index's membership degree r based on the actual index data ij , that is, first consider the impact of a single indicator on road traffic congestion, and determine which traffic state level it belongs to. The corresponding r of the traffic state level ij is 1, and does not belong to the traffic status level corresponding to r ij Taking the value as 0, we get the combined membership degree R, expressed as:

[0056]

[0057] Calculate the comprehensive evaluation matrix B of road congestion, expressed as:

[0058] B=W*R

[0059] Where W is w j The maximum membership degree principle is proposed, and the level with the maximum membership degree in the comprehensive evaluation matrix of road congestion is selected as the final congestion level to complete the evaluation of the traffic situation.

[0060] When the traffic is in a locked state, if the controlled connected vehicle leaves the original road and changes to the smart track, it will inevitably have a negative impact on the traffic situation of the section, so in this state, the vehicle will continue to drive in the original state.

[0061] When the traffic is in a non-locked state, the length and time of the vehicle changing lanes and occupying the smart track are obtained based on the protection headway, safety headway, the length of the controlled connected vehicle's dominant range and the average speed of vehicles with priority right of way.

[0062] The controlled vehicle needs to consider the protection headway and safety headway when changing lanes. The protection headway is determined by the safety headway of the corresponding vehicle type and the headway of the controlled vehicle at the corresponding time obtained according to the ICV following model; the safety headway is determined by the headway of the preceding vehicle and the shortest safety headway.

[0063] Among them, the protective headway time distance provides the rear vehicle (i.e. the controlled vehicle) with a comfortable headway time distance with the front vehicle (i.e. the vehicle with priority right of way). This can avoid the psychological pressure and burden on the driver of the front vehicle caused by the rear vehicle following too closely. Therefore, according to different vehicle types, the safe headway time distance t matching the vehicle type is obtained. safe In addition, the headway of the controlled vehicle n at the tth moment is obtained according to the ICV following model. The protection headway is the maximum value of the above two, expressed as:

[0064] U1=max(t safe ,Δx n (t))

[0065]

[0066] Among them, U1 is the time distance for protecting the head of the vehicle, t safe is the safe headway time of the controlled vehicle of the corresponding type, Δx n (t) is the headway, k 1 is the first coefficient, with a value of 1.0, k 2 is the second coefficient, with a value of 0.1, k 3 is the third coefficient, with a value of 0.58, Δx n (t) is the headway of vehicle n at the tth moment, v n (t) is the speed of vehicle n at the tth moment, v n-1 (t) is the speed of vehicle n-1 (relative to the front vehicle of the controlled vehicle) at the tth moment, d is the fifth coefficient, the value is 5.5, exp is the exponential function, m is the fourth coefficient, the value is 8.83m / s.

[0067] The safe headway can ensure the headway required by the rear vehicle to brake and ensure that the rear vehicle can stop safely when the front vehicle breaks down or the road surface is abnormal. If the rear vehicle is slow, the safe headway will be small, making the distance between the front and rear vehicles too close. In order to avoid this situation, a comfortable headway is set. The minimum value of the comfortable headway is defined as the shortest safe headway. The safe headway is the maximum value of the shortest safe headway and the forward headway, expressed as:

[0068] U2=max(t′ safe ,min safe )+1

[0069] Among them, t′ safe is the headway of the preceding vehicle, min safe are the shortest safe time distances, and these two values ​​are specific values ​​regulated by road traffic conditions.

[0070] The duration of the controlled connected vehicle's control range is obtained based on the lane change time. The lane change time of the vehicle t change (x|μ,σ) obeys the log-normal distribution, expressed as:

[0071]

[0072] Where, t change (x|μ,σ) represents the lane-changing time of the vehicle, μ is the mean of the lognormal distribution, σ is the standard deviation of the lognormal distribution, var(X) is the variance, and E(X) represents the mathematical expectation. For μ and σ under different traffic density conditions, when the traffic flow state ranges from 0 to 1500 veh / h, the corresponding μ = 1.783, σ = 0.178; when the traffic flow state ranges from 1500 to 3000 veh / h, the corresponding μ = 1.699, σ = 0.174; when the traffic flow state ranges from more than 3000 veh / h, the corresponding μ = 1.732, σ = 0.176.

[0073] Based on the lane-changing time of the vehicle, the duration of the controlled connected vehicle’s ruling range Con is obtained, which is expressed as:

[0074] Con=t chang +1

[0075] Based on the protection headway, safety headway and the field of view of the controlled connected vehicle’s control range, the length of the smart track occupied by the controlled vehicle is obtained, which is expressed as:

[0076] Z=Z1+Z2+Z3=(U+Con)*v=(U1+U2+Con)*v

[0077] Among them, Z represents the total length of the occupied smart track, R1 represents the corresponding section of the protection headway, R2 represents the corresponding section of the safety headway, R3 represents the controlled range of the connected vehicle, U is the headway to ensure rapid passage, including the protection headway U1 and the safety headway U2, Con is the length of the controlled range of the connected vehicle, and v is the average speed of high-level vehicles with priority right of way.

[0078] High-level vehicles cannot communicate directly with the smart rail, and they pass valid information to the roadside nodes, which then publish it separately.

[0079] Design an interoperable cross-domain traffic mode information association system, adopt wireless communication and new generation Internet technology, implement dynamic real-time information interaction between vehicles and roads, and realize effective coordination between high-level vehicles and smart rails. The cross-domain traffic mode information association system includes information function service module, information coordination processing module, and information interaction sharing module. Roadside nodes are set up in the system to communicate effectively with high-level vehicles and smart rails through interoperable links.

[0080] Then, multi-modal communication technology is designed to ensure that traffic entities on the road can communicate with each other at any time and any place. The multi-modal communication system includes mobile network system, wireless network system and dedicated network system. The communication mode of mobile network system is 3G / 4G / 5G, the communication mode of wireless network system is WIFI, and the communication mode of dedicated network system is RFID.

[0081] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only the specific implementation method of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A time-division multiplexing method for intelligent rail routes based on interoperability, characterized in that: include: Acquiring vehicle data through roadside equipment, wherein the vehicle data includes location coordinates; The vehicle data is processed based on the trajectory smoothing method to obtain the fitted vehicle trajectory; Based on the fuzzy evaluation algorithm, multiple traffic indicators are processed, and the traffic status level is evaluated according to the weights of multiple traffic indicators and the degree of membership of the corresponding indicators in the traffic status level; when the traffic is in a locked state level, the original state is maintained; When the traffic is in a non-locked state, the length of the vehicle changing lanes and occupying the smart track and the time of occupying the smart track are obtained according to the protection headway, the safety headway, the controlled connected vehicle's dominating range and the average speed of vehicles with priority right of way; wherein, the protection headway is determined by the safety headway of the corresponding vehicle type and the headway of the controlled vehicle at the corresponding moment obtained according to the ICV following model; the safety headway is determined by the front vehicle's headway and the shortest safety headway; and the controlled connected vehicle's dominating range duration is obtained based on the lane changing time.

2. The interoperable smart rail route time-division multiplexing method according to claim 1 is characterized in that: The vehicle data is processed based on the trajectory smoothing method to obtain a fitted vehicle trajectory, including: Acquire vehicle features in two adjacent frames of data, wherein the vehicle features include a vehicle head angle and a vehicle size; The similarity of the corresponding vehicles in the two frames of data is calculated based on the features, expressed as: Among them, sim represents the similarity of the features between the two vehicles, d1 and d2 represent the values ​​of the corresponding vehicle features in the two frames of data; When the similarity exceeds 0.85, it is considered that the corresponding vehicles in the previous and next frames of data overlap, and the trajectory points of the corresponding vehicle in the next frame are removed.

3. The interoperable smart rail route time-division multiplexing method according to claim 2 is characterized in that: Connect the trajectory points and find a straight line y=kx+b by the least squares method, so that the sum of the vertical distances from each data point to this straight line is minimized, calculate the optimal solution of the trajectory data line, complete the fitting of the vehicle trajectory, and calculate the slope k of the fitting line of adjacent trajectory points: In the formula, and are the average values ​​of x and y respectively; Calculate the intercept b of the fitting line of adjacent trajectory points: Get the fitted vehicle trajectory.

4. The interoperable smart rail route time-division multiplexing method according to claim 3 is characterized in that: Get n samples representing congestion status and m influencing indicators to form the original evaluation indicator matrix where x ij represents the jth influencing factor of the i-th sample (i=1,2,…,n; j=1,2,…,m); Calculate each index x ij The proportion of this factor index p ij : Calculate the entropy value e of the jth indicator j : Where k is determined by the number of samples n, let ln is the natural logarithm, and e j The value of should be greater than or equal to 0. Calculate the coefficient of difference g of the jth indicator j , the coefficient of variation is expressed as: g j =1-e j Calculate the weight w of each factor indicator j : w j The set of values ​​is W.

5. The interoperability-based smart rail route time-division multiplexing method according to claim 4 is characterized in that: Considering the impact of a single indicator on road traffic congestion, determine the traffic state level to which it belongs, and the corresponding r of the traffic state level ij is 1, and does not belong to the traffic status level corresponding to r ij Taking the value as 0, we get the combined membership degree R, expressed as:

6. The interoperable smart rail route time-division multiplexing method according to claim 5, characterized in that: Calculate the comprehensive evaluation matrix B of road congestion, expressed as: B=W*R The level with the maximum membership degree in the comprehensive evaluation matrix of road congestion is selected as the final congestion level to complete the evaluation of the traffic situation.

7. The interoperability-based smart rail route time-division multiplexing method according to claim 1, characterized in that: The protection headway is determined by the safety headway of the corresponding vehicle type and the headway of the controlled vehicle at the corresponding time obtained according to the ICV following model, including: U1=max(t safe ,Δx n (t)) Among them, U1 is the time distance for protecting the head of the vehicle, t safe is the safe headway time of the controlled vehicle of the corresponding type, Δx n (t) is the headway time, k1 is the first coefficient, the value is 1.0, k2 is the second coefficient, the value is 0.1, k3 is the third coefficient, the value is 0.58, Δx n (t) is the headway of vehicle n at the tth moment, v n (t) is the speed of vehicle n at the tth moment, v n-1 (t) is the speed of vehicle n-1 at the tth moment, d is the fifth coefficient, exp is the exponential function, and m is the fourth coefficient, which is 8.83m / s.

8. The interoperable smart rail route time-division multiplexing method according to claim 7, characterized in that: The safe headway is determined by the headway of the preceding vehicle and the shortest safe headway, including: U2=max(t′ safe ,min safe )+1 Among them, U2 is the safe headway, t′ safe is the headway of the preceding vehicle, min safe are the shortest safe time distances, and these two values ​​are specific values ​​regulated by road traffic conditions.

9. The interoperable smart rail route time-division multiplexing method according to claim 8, characterized in that: The duration of the controlled area of ​​the accused connected vehicle is obtained based on the lane change time, including: Con=t cha +1 Among them, t change (x|μ,σ) represents the lane-changing time of the vehicle, μ is the mean of the lognormal distribution, σ is the standard deviation of the lognormal distribution, var(X) is the variance, and E(X) represents the mathematical expectation.

10. The interoperable smart rail route time-division multiplexing method according to claim 9, characterized in that: The time that a vehicle occupies the smart track when changing lanes is expressed as: T=U1+U2+Con The length of the smart track occupied by a vehicle changing lanes is expressed as: Z=T*v Where v is the average speed of high-level vehicles with priority.

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