Interoperable-based smart rail route time-sharing multiplexing method

Abstract

The application belongs to the technical field of intelligent transportation, and particularly relates to a smart rail route time-sharing multiplexing method based on interoperation, which comprises the following steps: acquiring vehicle data through a roadside device, wherein the vehicle data comprises position coordinates; processing the vehicle data based on a trajectory smoothing method to obtain a fitted vehicle trajectory; processing multiple traffic indexes based on a fuzzy evaluation algorithm, and evaluating a traffic state level according to the weight of the multiple traffic indexes and the membership degree of the corresponding index in the traffic state level; when the traffic is in a lock state level, maintaining the original state for driving; when the traffic is in a non-lock state, obtaining the length of the vehicle lane changing and occupying the smart rail and the time of occupying the smart rail according to the protection headway, the safe headway, the controlled networked vehicle ruling range duration and the average driving speed of the vehicle with priority road right.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent transportation, and specifically relates to a time-sharing multiplexing method for a smart rail route based on interoperation. BACKGROUND

[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 is showing a rapid growth trend. Especially in first-tier cities, the number of motor vehicles is increasing year by year, which leads to a serious imbalance between traffic supply and demand. Especially during the morning and evening peak hours, due to the concentrated outbreak of the demand for a large number of citizens commuting, the existing road system is difficult to effectively cope with the sharp increase in traffic flow in a short period of time, thereby causing a continuous and serious congestion phenomenon. In the face of the increasingly severe urban traffic challenge, many places have taken a series of measures to try to alleviate this situation, including setting up bus-only lanes to ensure the smooth operation of public transportation tools. However, in actual operation, it is found that these special lanes are often idle during off-peak hours or when the frequency of bus use is low, thereby reducing the lane resources available for other social vehicles, not only reducing the overall road utilization rate, but sometimes even exacerbating the degree of traffic congestion in local areas.

[0003] In order to solve the above contradictions and more efficiently utilize the limited road space, it is particularly important to introduce the concept of flexible road right. Flexible road right is a technical means based on real-time traffic conditions to dynamically adjust the use right of different types of transportation tools on a specific road section, which aims to optimize the efficiency of the entire city's traffic flow by flexibly configuring road resources. As an efficient and environmentally friendly public transportation tool, smart rail plays an important role in many cities. The smart rail-only lane often causes waste of road resources during low-frequency use, especially in cases where traffic demand changes greatly, so a flexible road right management service system based on vehicle-road cooperation is designed to implement flexible road right strategies on smart rail-only lanes, tap the potential of existing infrastructure, and alleviate urban traffic pressure. SUMMARY

[0004] The present application is proposed based on the above-mentioned needs of the prior art, and the technical problem to be solved by the present application is to provide a time-sharing multiplexing method for a smart rail route based on interoperation to flexibly regulate the controlled vehicles to improve the running speed and relieve traffic pressure.

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

[0006] Provided is an intelligent rail route time-sharing multiplexing method based on interoperation, comprising: acquiring vehicle data through a roadside device, the vehicle data comprising position coordinates; processing the vehicle data based on a trajectory smoothing method to obtain a fitted vehicle trajectory; processing a plurality of traffic indicators based on a fuzzy evaluation algorithm to evaluate a traffic state level according to the weight of the plurality of traffic indicators and the membership degree of the corresponding indicator to the traffic state level; when the traffic is in a lock state level, maintaining the original state for driving; when the traffic is in a non-lock state, obtaining the length of the vehicle lane changing and the occupation of the intelligent rail and the time of the occupation of the intelligent rail according to the protection headway, the safe headway, the controlled networked vehicle domination range time length and the average driving speed of the vehicle with priority road right; wherein the protection headway is determined by the safe 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 safe headway is determined by the headway of the front vehicle and the shortest safe time; and the controlled networked vehicle domination range time length is obtained based on the lane changing time.

[0007] Preferably, the trajectory smoothing method is used to process the vehicle data to obtain a fitted vehicle trajectory, comprising: acquiring vehicle features in adjacent two frames of data, the vehicle features comprising a vehicle head turning angle and a vehicle size; calculating the similarity of the corresponding vehicles in the two frames of data according to the features, expressed as: wherein 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 two frames of data coincide, and the trajectory point of the corresponding vehicle in the latter frame is removed.

[0008] The above setting is used to remove the repeatedly recorded vehicle trajectory, and the interference is reduced to improve the accuracy.

[0009] Preferably, the trajectory points are connected, a straight line y=kx+b is found by the least square method, so that the sum of the perpendicular distances of each data point to the straight line is minimized, the optimal solution of the line of the trajectory data is calculated, the fitting of the vehicle trajectory is completed, and the slope k of the fitting straight line of the adjacent trajectory points is calculated. wherein and are the average values of x and y, respectively; the intercept b of the fitting straight line of the adjacent trajectory points is calculated. The fitted vehicle trajectory is obtained.

[0010] Preferably, n samples representing congestion states are acquired, and m influence indicators are acquired to form an original evaluation indicator matrix wherein x ij represents the jth influence factor of the ith sample (i=1, 2, …, n; j=1, 2, …, m); each indicator x ij occupies the proportion p of the factor indicatorij : The entropy value e of the jth index is calculated j : Wherein k is determined by the sample number n, let ln is the natural logarithm, and e j The value of the jth index should be greater than or equal to 0, and the difference coefficient g of the jth index is calculated j The difference coefficient is represented as: g j =1-e j The weight w of each factor index is calculated j : w j The set of values is W.

[0011] Preferably, considering the influence of a single index on the road traffic congestion situation, the traffic state level to which it belongs is judged, and the traffic state level to which it belongs corresponds to r ij =1, and the traffic state level to which it does not belong corresponds to r ij =0, and the combination R of the membership degree is obtained, which is represented as:

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

[0013] Preferably, the protection headway is determined by the safety headway corresponding to the vehicle type and the headway of the controlled vehicle at the corresponding moment obtained according to the ICV following model, and comprises: Wherein, U1 is the protection headway, t safe is the safety headway of the controlled vehicle corresponding to the type, Δx n (t) is the headway, 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, the value is 5.5, exp is the exponential function, and m is the fourth coefficient, the value is 8.83 m / s.

[0014] Preferably, the safety headway is determined by the headway of the preceding vehicle and the minimum safety headway, and comprises: U2=max(t′ safe ,min safe )+1, wherein U2 is the safety headway, t′ safe is the headway of the preceding vehicle, minsafe For the shortest safety time interval, the two values are specific values specified by road traffic conditions.

[0015] Preferably, the controlled networked vehicle dominion duration is obtained based on the lane changing time, including: Con=t change +1, Wherein, t change (x|mu, sigma) represents the lane changing time of the vehicle, mu is the mean of the lognormal distribution, sigma is the standard deviation of the lognormal distribution, var(x) is the variance, and E(x) represents the mathematical expectation.

[0016] Preferably, the time of the vehicle lane changing occupying the smart track is represented as: T=U1+U2+Con, and the length of the vehicle lane changing occupying the smart track is represented as: Z=T*v, wherein v is the average driving speed of the high-level vehicle with priority right.

[0017] Compared with the prior art, the present application can consider which traffic indicator has greater influence on the traffic situation under different traffic conditions by using the multi-index fuzzy evaluation algorithm, and then evaluate the traffic situation based on this, so as to eliminate the interference of other factors on the traffic state and judge the traffic state. In addition, more fine-grained calculation is performed in the smart track right range. Through the above setting, 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, and can improve the traffic efficiency to a greater extent. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present specification or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present specification, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0019] Figure 1 The step flowchart of the smart track route time division multiplexing method based on interoperation provided in the present application;

[0020] Figure 2 The relationship between the index data and the threshold value in the embodiment of the present application is shown in the schematic diagram. DETAILED DESCRIPTION

[0021] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0022] In the description of the embodiments of the present application, it should be noted that, unless otherwise explicitly specified and limited, the term "connected" should be understood broadly, for example, it can be fixedly connected, or detachably connected, or integrally connected, which can be mechanically connected, or electrically connected, which can be directly connected, or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meaning of the above-mentioned term in the present application can be understood according to the specific circumstances.

[0023] The terms "top", "bottom", "above", "under" and "on" used throughout the description are relative positions of components of the device, for example, the relative positions of the top and bottom substrates inside the device. It can be understood that the device is multifunctional, regardless of their orientation in space.

[0024] In order to facilitate the understanding of the embodiments of the present application, the following will be further explained and described with specific embodiments in conjunction with the drawings, and the embodiments do not constitute a limitation on the embodiments of the present application.

[0025] The present embodiment provides an intelligent rail route time-sharing multiplexing method based on interoperation, as shown in Figure 1 .

[0026] The intelligent rail route time-sharing multiplexing method based on interoperation comprises:

[0027] Obtain vehicle data through a roadside device.

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

[0029] For single-vehicle travel trajectory, coordinate offset and coordinate jitter generated by vehicle detection by the roadside device are very common and difficult to avoid. This may cause a vehicle to be recorded with multiple coincident trajectory data or positionally close redundant data at the same time, and there may be cases where part of the trajectory points have a large deviation from the position of the adjacent trajectory points in the trajectory data, which will cause the risk research utility to decrease in multiple scenarios. Therefore, a trajectory smoothing algorithm is added, which not only adapts to the detection accuracy of different roadside device sensors, but also meets the online processing needs of a large amount of high-precision data.

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

[0031] Trajectory smoothing adopts a spline curve optimization algorithm. First, continuous data points (x i ,y i ) of vehicle coordinates are obtained by roadside equipment, and 10 frames of data are uploaded in unit time. Then, data reconstruction is performed, and the trajectory is removed. The similarity between the two vehicles is calculated (turning angle, vehicle size):

[0032]

[0033] In the formula, sim represents the similarity between the two vehicles, d1 and d2 represent the specific values of the features that need to be calculated for two frames of data, and these values are obtained by roadside equipment. The similarity threshold is set to 0.85, that is, when the similarity exceeds 0.85, it is considered that the two are coincident, and the trajectory point at the later time is removed.

[0034] The acceptable offset of the current road is set, the vehicle coordinate points returned by the roadside equipment are connected, and a straight line y=kx+b is found by the least squares method, so that the sum of the perpendicular distances of each data point to the straight line is minimized, thus calculating the optimal solution of the trajectory data line, completing the fitting of the vehicle trajectory. Calculate the slope k of the adjacent trajectory point fitting straight line:

[0035]

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

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

[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 weight of multiple traffic indicators and the membership degree of the corresponding indicator in the traffic state level. When the traffic is in a locked state level, keep the original state driving.

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

[0042] In order to improve the judgment of the traffic state level, multiple traffic indicators are introduced, and through the fuzzy evaluation algorithm of multiple indicators, it is considered which indicator has greater influence on the traffic situation under different traffic states, and the traffic situation is evaluated based on the indicator, so as to accurately obtain the traffic state level.

[0043] Specifically, n samples representing congestion states and m influence indicators are obtained to form an original evaluation index matrix Wherein x ij represents the jth influence factor of the ith sample (i=1, 2,..., n; j=1, 2,..., m).

[0044] The value of each index x ij of the factor index is calculated. ij :

[0045]

[0046] The entropy value e j of the jth index is calculated.

[0047]

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

[0049] The difference coefficient g j of the jth index is calculated, and the larger the difference coefficient, the greater the influence of the value of the factor index on the evaluation result, and vice versa. The difference coefficient is represented as:

[0050] g j =1-e j

[0051] The weight w j of each factor index is calculated.

[0052]

[0053] The threshold of each index is set, specifically, each index has a number Y(x)∈[0, 1] corresponding to it, that is, Y is the fuzzy set corresponding to the index, and Y(x) is the membership degree of the index to Y. The closer the membership degree is to 1, the higher the degree of the index belonging to Y, and vice versa. When the membership degree is closer to 0, the degree of the index belonging to Y is lower. For example Figure 2As shown in the figure, k1, k2, k3, …, k10 represent linear values of a certain index close to the threshold value. According to the position of the actual value of the index in the above k values, for example, if the value is between k1 and k2, the index has the membership degree of two traffic state levels, and if the value is between k2 and k3, the index has the membership degree of one traffic state level.

[0054] In one possible implementation of the embodiment, the indexes include traffic flow density and road saturation, and the values of the two indexes will increase when the traffic congestion level is higher, so that for Figure 2 , the corresponding traffic state levels from left to right are very smooth, smooth, light congestion, moderate congestion, severe congestion, and lock. When the traffic state level is higher, the accuracy of judging whether the traffic is locked will be higher.

[0055] Determine the membership degree r of the index according to the actual index data ij , that is, only consider the influence of a single index on the road traffic congestion, judge which traffic state level it belongs to, and the traffic state level corresponding to r ij is 1, and the traffic state level corresponding to r ij is 0, and obtain the combination R of the membership degrees, which is represented as:

[0056]

[0057] Calculate the road congestion comprehensive evaluation matrix B, which is represented as:

[0058] B = W * R

[0059] Wherein, W is a set of w j values. The maximum membership degree principle is proposed, the level where the maximum membership degree in the road congestion comprehensive evaluation matrix is selected as the final congestion level, and the evaluation of the traffic situation is completed.

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

[0061] When the traffic is in the non-lock state, according to the protection head time, the safe head time, the controlled vehicle range time, and the average driving speed of the vehicle with priority road right, the length of the vehicle changing lane and occupying the track and the time of occupying the track are obtained.

[0062] The lane changing of the controlled vehicle needs to consider the protection headway and the safe headway. The protection headway is determined by the safe headway corresponding to the vehicle type and the headway of the controlled vehicle at the corresponding moment according to the ICV following model; and the safe headway is determined by the front vehicle headway and the shortest safe time.

[0063] The protection headway provides the rear vehicle (i.e. the controlled vehicle) with a comfortable following headway with the front vehicle (i.e. the priority right vehicle). Thus, the psychological pressure and burden of the driver of the front vehicle caused by the rear vehicle following too close can be avoided. According to different vehicle types, the safe headway t safe In addition, the headway of the controlled vehicle n at the tth moment is obtained according to the ICV following model, and the protection headway is the maximum of the above two, which is expressed as:

[0064] U1 = max (t safe , Δx n (t))

[0065]

[0066] The protection headway U1, the safe headway t safe of the controlled vehicle corresponding to the type, the headway Δx n (t), the first coefficient k1 with a value of 1.0, the second coefficient k2 with a value of 0.1, the third coefficient k3 with a value of 0.58, the headway Δx n (t) of the vehicle n at the tth moment, the speed v n (t) of the vehicle n at the tth moment, the speed v n-1 (t) of the vehicle n-1 (relative to the front vehicle of the controlled vehicle) at the tth moment, the fifth coefficient d with a value of 5.5, the exponential function exp, and the fourth coefficient m with a value of 8.83 m / s.

[0067] The safe headway can ensure the headway required for the rear vehicle to brake and can ensure the safe stopping of the rear vehicle when the front vehicle fails or the road surface is abnormal. If the speed of the rear vehicle is slow, the safe headway will be small, which will make the distance between the front vehicle and the rear vehicle too close. In order to avoid this situation, the comfortable headway is set, and the minimum value of the comfortable headway is defined as the shortest safe time. The safe headway is the maximum of the shortest safe time and the front headway, which is expressed as:

[0068] U2 = max (t′ safe , min safe )+1

[0069] The front vehicle headway t′ safe , the minimumsafe For the shortest safety time interval, the two values are specific values specified by road traffic conditions.

[0070] The controlled connected vehicle dominance range duration is obtained based on the lane changing time of the vehicle. change (x|μ,σ) is subject to a lognormal distribution, expressed as:

[0071]

[0072] In the formula, 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 density traffic conditions, including when the traffic flow state ranges from 0 to 1500 veh / h, the corresponding μ=1.783 and σ=0.178; when the traffic flow state ranges from 1500 to 3000 veh / h, the corresponding μ=1.699 and σ=0.174; when the traffic flow state is greater than 3000 veh / h, the corresponding μ=1.732 and σ=0.176.

[0073] The controlled connected vehicle dominance range duration Con is obtained based on the lane changing time of the vehicle, expressed as:

[0074] Con=t chang +1

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

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

[0077] Wherein, Z represents the total length of the occupied smart track, R1 represents the road section corresponding to the protection headway, R2 represents the road section corresponding to the safety headway, R3 represents the controlled connected vehicle dominance range, U is the headway for ensuring fast passage, including the protection headway U1 and the safety headway U2, Con is the controlled connected vehicle dominance range duration, and v is the average driving speed of the high-level vehicle with priority road right.

[0078] The high-level vehicle cannot directly communicate with the smart track, and the effective information is transmitted to the roadside node, and then the roadside node is published again.

[0079] The cross-domain traffic mode information association system based on interoperation is designed, wireless communication and new generation Internet technology are adopted, dynamic real-time information interaction between vehicle-to-vehicle and vehicle-to-road is implemented, and effective cooperation between high-level vehicles and smart rails is realized. The cross-domain traffic mode information association system comprises an information function service module, an information cooperation processing module and an information interaction sharing module. Roadside nodes are arranged in the system, and effective communication with high-level vehicles and smart rails is realized through interoperation links.

[0080] Then, a multi-mode communication technology is designed to ensure that traffic subjects on the road can interconnect at any time, any place and between different traffic subjects. The multi-mode communication system comprises a mobile network system, a wireless network system and a special network system. The communication mode of the mobile network system is 3G / 4G / 5G, the communication mode of the wireless network system is WIFI, and the communication mode of the special network system is RFID.

[0081] The above specific embodiments further illustrate the purpose, technical solutions and advantages of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A time-division multiplexing method for intelligent rail transit routes based on interoperability, characterized in that, include: Vehicle data of the controlled connected vehicles is acquired through roadside equipment, and the vehicle data includes location coordinates; Vehicle data is processed using a trajectory smoothing method to obtain a fitted vehicle trajectory. The fuzzy evaluation algorithm is used to process multiple traffic indicators. The traffic state level is evaluated based on the weight 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 locked state, the original state is maintained. When traffic is not locked, the length of the vehicle changing lanes and occupying the intelligent rail and the duration of occupation are obtained based on the protected headway, the safe headway, the duration of the controlled connected vehicle's dominance range, and the average speed of vehicles with priority right-of-way. The vehicles with priority right-of-way are those occupying dedicated intelligent rail lanes. The protected headway is determined by the safe headway for the corresponding vehicle type and the headway of the controlled vehicle at the corresponding moment, obtained from the ICV car-following model, including: U1=max(t safe ,Δx n (t)) Where U1 is the headway for protecting the front of the vehicle, t safe Δx represents the safe headway for the controlled vehicle of the corresponding type. n (t) represents the headway, k1 is the first coefficient with a value of 1.0, k2 is the second coefficient with a value of 0.1, k3 is the third coefficient with a value of 0.58, and Δx n (t) represents the headway of vehicle n at time t, v n (t) represents the velocity of vehicle n at time t, v n-1 (t) represents the speed of vehicle n-1 at time t, d is the fifth coefficient, exp is the exponential function, and m is the fourth coefficient, with a value of 8.83 m / s; The safe headway is determined by the headway of the vehicle ahead and the minimum safe headway, including: U2=max(t′ safe ,min safe )+1 Where U2 is the safe headway, t′ safe The time distance to the front of the vehicle in min safe These two values ​​are specific values ​​determined by road traffic conditions to represent the shortest safe travel time. The duration of the controlled connected vehicle's dominance range is obtained based on lane-changing 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 log-normal distribution, σ is the standard deviation of the log-normal distribution, var(X) is the variance, and E(X) represents the expected value.

2. The interoperability-based intelligent rail transit route time-division multiplexing method according to claim 1, characterized in that, The process of processing vehicle data based on the trajectory smoothing method to obtain the fitted vehicle trajectory includes: Obtain vehicle features from two adjacent frames of data, including vehicle front angle and vehicle size; The similarity between corresponding vehicles in two frames of data is calculated based on features, and expressed as: Where sim represents the similarity of features between the two vehicles, and d1 and d2 represent the values ​​of the corresponding vehicle features in the two frames of data; When the similarity exceeds 0.85, the corresponding vehicles in the two frames are considered to overlap, and the trajectory points of the corresponding vehicles in the second frame are removed.

3. The interoperability-based intelligent rail transit route time-division multiplexing method according to claim 2, characterized in that, Connect the trajectory points and use the least squares method to find a straight line y = kx + b that minimizes the sum of the perpendicular distances from each data point to this line. Calculate the optimal solution for the trajectory data, complete the fitting of the vehicle trajectory, and calculate the slope k of the fitted line between adjacent trajectory points. In the formula, and These are the average values ​​of x and y, respectively. Calculate the intercept b of the fitted line between adjacent trajectory points: The fitted vehicle trajectory is obtained.

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

5. The interoperability-based intelligent rail transit route time-division multiplexing method according to claim 4, 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 traffic state level r. ij A value of 1 indicates that the traffic state level r does not belong to this category. ij When the value is 0, the membership degree associativity R is obtained, which is represented as:

6. The interoperability-based intelligent rail transit route time-division multiplexing method according to claim 5, characterized in that, The comprehensive road congestion evaluation matrix B is calculated as follows: B = W * R The final congestion level is determined by selecting the level with the highest membership value in the comprehensive road congestion evaluation matrix, thus completing the evaluation of the traffic situation.

7. The interoperability-based intelligent rail transit route time-division multiplexing method according to claim 1, characterized in that, The time a vehicle occupies the intelligent rail system when changing lanes is expressed as follows: T = U1 + U2 + Con The length of the intelligent rail transit occupied by a vehicle changing lanes is represented as follows: Z = T * v Where v is the average speed of high-level vehicles with priority right-of-way.

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