WO2023216793A1 - Dynamic speed limit control method for highway bottleneck section in mixed traffic flow environment - Google Patents
Dynamic speed limit control method for highway bottleneck section in mixed traffic flow environment Download PDFInfo
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Abstract
A dynamic speed limit control method for a highway bottleneck section in a mixed traffic flow environment, comprising: S1, identifying a highway bottleneck section by using a traffic event detection device or a construction operation reporting system; S2, setting a speed limit control period and a model prediction period; S3, dividing a controlled section according to a region where the bottleneck section is located; S4, collecting traffic flow data of a highway section to be controlled by using a traffic flow monitoring device; S5, optimizing a cell transmission model according to the collected traffic flow data and traffic flow characteristics of a normal state, a speed-limited state, and a bottleneck section of a highway in a mixed traffic flow environment to obtain an improved cell-transmission model; and S6, selecting an optimal speed limit value according to the improved cell-transmission model, and publishing the optimal speed limit value by means of a dynamic speed limit control system.
Description
本发明属于交通安全和智能交通控制领域，具体涉及一种混合交通流环境高速公路瓶颈路段动态限速控制方法。The invention belongs to the fields of traffic safety and intelligent traffic control, and specifically relates to a dynamic speed limit control method for a bottleneck section of a highway in a mixed traffic flow environment.
随着交通需求的提高，高速公路车流量不断增加，高速公路交通拥堵的发生也越来越频繁。众所周知，交通拥堵会导致道路服务水平和交通安全性下降增加。近年来，自动驾驶技术迅猛发展，自动驾驶车辆从车辆微观层面改善车辆动态特性、缩短跟车距离，有望从根本上改变传统交通流的基本属性，为提升交通安全和通行效率提供新思路。然而，从人工驾驶时代进化到完全自动驾驶时代需要一个漫长的发展过程，在今后很长一段时间内，人工驾驶和自动驾驶车辆同时在道路上行驶将成为未来交通系统的一个重要特征。As traffic demand increases, highway traffic volume continues to increase, and highway traffic congestion occurs more and more frequently. It is well known that traffic congestion leads to increased degradation in road service levels and traffic safety. In recent years, autonomous driving technology has developed rapidly. Autonomous vehicles can improve vehicle dynamic characteristics and shorten the following distance from the micro level of the vehicle. It is expected to fundamentally change the basic attributes of traditional traffic flow and provide new ideas for improving traffic safety and traffic efficiency. However, the evolution from the era of manual driving to the era of fully autonomous driving requires a long development process. For a long time to come, the simultaneous driving of manual and autonomous vehicles on the road will become an important feature of the future transportation system.
高速公路上因事故、施工等事件产生的瓶颈路段是交通拥堵的重点区域，高速公路瓶颈路段交通流存在车辆频繁加减速等不良特性，混合交通流环境下，交通流特性更加复杂，可能会对高速公路行车安全会造成严重影响。因此，寻找在人工-自动驾驶混合交通流环境下合理、有效的高速公路瓶颈路段控制方法对于提高瓶颈路段的行车安全具有一定意义。Bottleneck sections on highways caused by accidents, construction and other events are key areas for traffic congestion. Traffic flow in bottleneck sections of highways has undesirable characteristics such as frequent acceleration and deceleration of vehicles. In a mixed traffic flow environment, traffic flow characteristics are more complex, which may affect the traffic flow. Highway driving safety can have serious consequences. Therefore, finding a reasonable and effective control method for highway bottleneck sections in a mixed traffic flow environment of manual and autonomous driving is of certain significance to improve the driving safety of bottleneck sections.
在传统交通流环境下，使用动态限速控制的方法来缓解高速公路交通拥堵的应用非常广泛，并取得了较好的反馈结果。现有的动态限
速方法多是基于传统宏观或微观交通流模型设计的，对于大多数专注于高速公路路段的动态限速控制系统，宏观模型将更适用于提供更短的控制间隔。传统宏观交通流模型未考虑自动驾驶车辆的行驶特性，在人工和自动驾驶车辆混合的环境下，高速公路的交通流特性发生根本改变，基于传统交通流模型的瓶颈路段动态限速方法难以继续适用。因此，需要开发一种适用于人工自动驾驶混合交通流环境下的高速公路瓶颈路段动态限速控制方法，来提高高速公路的瓶颈路段的行车安全及效率。In traditional traffic flow environments, the use of dynamic speed limit control methods to alleviate highway traffic congestion is widely used and has achieved good feedback results. Existing dynamic limits Speed methods are mostly designed based on traditional macro or micro traffic flow models. For most dynamic speed limit control systems focusing on highway sections, macro models will be more suitable for providing shorter control intervals. Traditional macroscopic traffic flow models do not consider the driving characteristics of autonomous vehicles. In a mixed environment of manual and autonomous vehicles, the traffic flow characteristics of highways have fundamentally changed. The dynamic speed limit method of bottleneck sections based on traditional traffic flow models is difficult to continue to apply. . Therefore, it is necessary to develop a dynamic speed limit control method for highway bottleneck sections suitable for manual and autonomous driving mixed traffic flow environments to improve the driving safety and efficiency of highway bottleneck sections.
发明内容Contents of the invention
为了解决现有技术中动态限速的主要手段未考虑混合交通流环境而导致限速值不合理的技术问题，本发明的目的在于提供一种混合交通流环境高速公路瓶颈路段动态限速控制方法。In order to solve the technical problem that the main means of dynamic speed limiting in the prior art does not consider the mixed traffic flow environment, resulting in unreasonable speed limit values, the purpose of the present invention is to provide a dynamic speed limit control method for a bottleneck section of a highway in a mixed traffic flow environment. .
为了实现上述发明目的，本发明采用如下所述的技术方案：In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:
一种混合交通流环境高速公路瓶颈路段动态限速控制方法，包括如下步骤：A dynamic speed limit control method for bottleneck sections of expressways in mixed traffic flow environments, including the following steps:
S1、在人工-自动驾驶混合交通流环境下，利用交通事件检测设备或施工作业上报系统对高速公路瓶颈路段进行识别；S1. In a mixed traffic flow environment of manual and autonomous driving, use traffic incident detection equipment or construction operation reporting system to identify highway bottleneck sections;
S2、识别完成后，设置限速控制周期及模型预测周期，模型预测周期一般为限速控制周期的整数倍，限速控制周期为5-10min；S2. After the identification is completed, set the speed limit control period and model prediction period. The model prediction period is generally an integer multiple of the speed limit control period, and the speed limit control period is 5-10 minutes;
S3、根据瓶颈路段所在区域划分控制路段，一般将瓶颈路段上游500-700m记为缓冲路段；将缓冲路段上游5-20km设置为限速路段，
限速路段分段间隔1-5km；S3. Divide control sections according to the area where the bottleneck section is located. Generally, 500-700m upstream of the bottleneck section is recorded as a buffer section; 5-20km upstream of the buffer section is set as a speed limit section. Speed limit sections are spaced 1-5km apart;
S4、利用交通流监测设备对高速公路待控制路段的交通流数据进行采集，包括人工、自动驾驶车辆比例、车道占用情况、速度、流量、密度和当前限速值，采集频率一般根据模型控制周期设置，需小于或等于模型控制周期；S4. Use traffic flow monitoring equipment to collect traffic flow data on the section of the highway to be controlled, including the proportion of manual and autonomous vehicles, lane occupancy, speed, flow, density and current speed limit value. The collection frequency is generally based on the model control cycle Setting, needs to be less than or equal to the model control period;
S5、根据采集的交通流数据和混合交通流环境下高速公路正常、限速及瓶颈路段的交通流特性对元胞传输模型进行优化，得到改进元胞传输模型，基于采集的实时交通流数据，预测混合交通流环境下各限速方案控制下下一周期内的交通流数据；S5. Optimize the cellular transmission model based on the collected traffic flow data and the traffic flow characteristics of normal, speed limit and bottleneck sections of the highway in a mixed traffic flow environment, and obtain an improved cellular transmission model. Based on the collected real-time traffic flow data, Predict the traffic flow data in the next period under the control of each speed limit scheme in a mixed traffic flow environment;
S6、根据改进的元胞传输模型选出最优限速值，将最优限速值通过动态限速控制系统进行发布；S6. Select the optimal speed limit value based on the improved cellular transmission model, and publish the optimal speed limit value through the dynamic speed limit control system;
S7、重复S4、S5、S6步再次确定最优限速值，实现混合交通流环境高速公路瓶颈路段动态限速控制。S7. Repeat steps S4, S5, and S6 to determine the optimal speed limit value again to achieve dynamic speed limit control on the bottleneck section of the expressway in a mixed traffic flow environment.
进一步的，在步骤S5中，所述根据采集的交通流数据和混合交通流环境下高速公路正常、限速及瓶颈路段的交通流特性对元胞传输模型进行优化，得到改进元胞传输模型，包括：Further, in step S5, the cellular transmission model is optimized based on the collected traffic flow data and the traffic flow characteristics of normal, speed limit and bottleneck sections of the highway in a mixed traffic flow environment to obtain an improved cellular transmission model. include:
根据元胞传输模型要求将瓶颈路段、缓冲路段及限速路段记为控制路段m，将m路段以单位长度Lm为单位划分为Nm个元胞；According to the requirements of the cell transmission model, bottleneck sections, buffer sections and speed limit sections are recorded as control section m, and the m section is divided into N m cells with unit length L m as the unit;
(1)搭建人工-自动驾驶混合交通流元胞传输模型，即MCTM模型(1) Build a manual-autonomous driving hybrid traffic flow cell transmission model, namely the MCTM model
人工驾驶跟驰模型中，车头间距sr与平均车速v之间的关系式为：
sr＝trv+dr In the manual driving car-following model, the relationship between the head-to-head distance s r and the average vehicle speed v is:
s r =t r v + d r
sr＝trv+dr In the manual driving car-following model, the relationship between the head-to-head distance s r and the average vehicle speed v is:
s r =t r v + d r
tr为人工驾驶反应延时；dr为人工驾驶位移差项；t r is the manual driving reaction delay; d r is the manual driving displacement difference term;
自动驾驶跟驰模型中，ta为自动驾驶期望车头时距；da为自动驾驶拥挤状态车头间距，交通流约束关系为：
sa＝tav+da In the automatic driving car-following model, t a is the expected headway of automatic driving; d a is the headway of automatic driving in crowded conditions. The traffic flow constraint relationship is:
s a =t a v+d a
sa＝tav+da In the automatic driving car-following model, t a is the expected headway of automatic driving; d a is the headway of automatic driving in crowded conditions. The traffic flow constraint relationship is:
s a =t a v+d a
人工驾驶和自动驾驶混合状态下的交通流可通过平均所有车辆车头间距的方式来分析不同自动驾驶车辆比例下的混合交通流基本图特性，使用p＝(p1,p2)分别为两类交通流的比例，p1+p2＝1，各类车型比例为p时，阻塞密度表示为：
Traffic flow in a mixed state of manual driving and automatic driving can be analyzed by averaging the distance between all vehicles to analyze the basic diagram characteristics of mixed traffic flow under different proportions of automatic driving vehicles. Use p = (p 1 , p 2 ) to divide the two categories respectively. The proportion of traffic flow, p 1 + p 2 = 1, and the proportion of various vehicle types is p, the congestion density is expressed as:
Traffic flow in a mixed state of manual driving and automatic driving can be analyzed by averaging the distance between all vehicles to analyze the basic diagram characteristics of mixed traffic flow under different proportions of automatic driving vehicles. Use p = (p 1 , p 2 ) to divide the two categories respectively. The proportion of traffic flow, p 1 + p 2 = 1, and the proportion of various vehicle types is p, the congestion density is expressed as:
临界密度表示为：
The critical density is expressed as:
The critical density is expressed as:
反向波速表示为：
The reverse wave speed is expressed as:
The reverse wave speed is expressed as:
最大通行能力表示为：
The maximum traffic capacity is expressed as:
The maximum traffic capacity is expressed as:
元胞传输模型需要对交通流进行时空离散化处理，将m路段以单位长度Lm为单位划分为Nm个元胞，在混合交通流情况下，元胞i∈{1,2,…,Nm}在第k∈{0,1,2,…,K}个时间间隔(时间间隔为Δt)的
交通流参数包括：平均速度vm,i(k)、元胞驶入流率qm,i-1(k)、驶出流率qm,i(k)、车流密度ρm,i(k)，根据元胞传输模型，平均速度可表示为：
The cellular transmission model requires spatio-temporal discretization of the traffic flow. The m road section is divided into N m cells with unit length L m . In the case of mixed traffic flow, the cell i∈{1,2,…, N m } in the k∈{0,1,2,…,K}th time interval (the time interval is Δt) Traffic flow parameters include: average speed v m,i (k), cell entry flow rate q m,i-1 (k), exit flow rate q m,i (k), traffic flow density ρ m,i ( k), according to the cell transmission model, the average speed can be expressed as:
The cellular transmission model requires spatio-temporal discretization of the traffic flow. The m road section is divided into N m cells with unit length L m . In the case of mixed traffic flow, the cell i∈{1,2,…, N m } in the k∈{0,1,2,…,K}th time interval (the time interval is Δt) Traffic flow parameters include: average speed v m,i (k), cell entry flow rate q m,i-1 (k), exit flow rate q m,i (k), traffic flow density ρ m,i ( k), according to the cell transmission model, the average speed can be expressed as:
使用λm表示车道数，流出速率可表示为：
qm,i(k)＝ρm,i(k)vm,i(k)λm Using λ m to represent the number of lanes, the outflow rate can be expressed as:
q m,i (k)＝ρ m,i (k)v m,i (k)λ m
qm,i(k)＝ρm,i(k)vm,i(k)λm Using λ m to represent the number of lanes, the outflow rate can be expressed as:
q m,i (k)＝ρ m,i (k)v m,i (k)λ m
元胞i的车辆数可表示为：
nm,i(k+1)＝nm,i(k)+ym,i-1(k)-ym,i(k)The number of vehicles in cell i can be expressed as:
n m,i (k+1)＝n m,i (k)+y m,i-1 (k)-y m,i (k)
nm,i(k+1)＝nm,i(k)+ym,i-1(k)-ym,i(k)The number of vehicles in cell i can be expressed as:
n m,i (k+1)＝n m,i (k)+y m,i-1 (k)-y m,i (k)
其中，nm,i(k)为元胞内原有车辆数，ym,i-1(k)为流入车辆数、ym,i(k)为流出车辆数，驾驶该路段无流入流出匝道，流入量和流出量计算公式为：
ym,i(k)＝qm,i(k)ΔtAmong them, n m,i (k) is the original number of vehicles in the cell, y m,i-1 (k) is the number of incoming vehicles, y m,i (k) is the number of outgoing vehicles, and there are no incoming and outgoing ramps on this road section. , the calculation formula for inflow and outflow is:
y m,i (k)＝q m,i (k)Δt
ym,i(k)＝qm,i(k)ΔtAmong them, n m,i (k) is the original number of vehicles in the cell, y m,i-1 (k) is the number of incoming vehicles, y m,i (k) is the number of outgoing vehicles, and there are no incoming and outgoing ramps on this road section. , the calculation formula for inflow and outflow is:
y m,i (k)＝q m,i (k)Δt
根据交通流理论，元胞i的车辆数也可表示为：
nm,i(k+1)＝ρm,i(k+1)Lmλm According to traffic flow theory, the number of vehicles in cell i can also be expressed as:
n m,i (k+1)＝ρ m,i (k+1)L m λ m
nm,i(k+1)＝ρm,i(k+1)Lmλm According to traffic flow theory, the number of vehicles in cell i can also be expressed as:
n m,i (k+1)＝ρ m,i (k+1)L m λ m
元胞i在k+1时刻的密度可表示为：
The density of cell i at time k+1 can be expressed as:
The density of cell i at time k+1 can be expressed as:
根据上述公式推导，可计算出不同人工和自动驾驶车辆比例情况下，混合交通流当前元胞在下一时刻的车辆数、密度等交通流参数，再遍历整个元胞层与时间层，可计算混合交通流的时空交通状态演化，
完成MCTM建模；According to the above formula derivation, the traffic flow parameters such as the number and density of vehicles in the current cell of the mixed traffic flow at the next moment can be calculated under different proportions of manual and autonomous vehicles. Then the entire cell layer and time layer can be traversed to calculate the mixed traffic flow parameters. The spatiotemporal traffic state evolution of traffic flow, Complete MCTM modeling;
(2)搭建动态限速MCTM模型，即DMCTM模型(2) Build a dynamic speed limit MCTM model, that is, the DMCTM model
使用vdsl表示动态限速值，平均速度可表示为：
Using vdsl to represent the dynamic speed limit value, the average speed can be expressed as:
Using vdsl to represent the dynamic speed limit value, the average speed can be expressed as:
动态限速条件下,最大通行能力与临界密度之间的关系可表示为：
Maximum traffic capacity under dynamic speed limit conditions with critical density The relationship between them can be expressed as:
Maximum traffic capacity under dynamic speed limit conditions with critical density The relationship between them can be expressed as:
流入元胞的流率可表示为：
The flow rate into the cell can be expressed as:
The flow rate into the cell can be expressed as:
密度根据车辆数守恒推导获得，根据上述DMCTM模型公式可递推得到任一时空交通流基本参数；The density is derived based on the conservation of the number of vehicles. According to the above DMCTM model formula, the basic parameters of any space-time traffic flow can be obtained recursively;
(3)搭建瓶颈路段MCTM模型，即BMCTM模型(3) Build the MCTM model of the bottleneck section, that is, the BMCTM model
瓶颈路段的流出量：
Outflow volume at the bottleneck section:
Outflow volume at the bottleneck section:
其中，γ为通行能力下降幅度，根据交通流理论，瓶颈路段的交通流速度vm,i(k)表示为：
in, γ is the decrease in traffic capacity. According to the traffic flow theory, the traffic flow speed v m,i (k) of the bottleneck section is expressed as:
in, γ is the decrease in traffic capacity. According to the traffic flow theory, the traffic flow speed v m,i (k) of the bottleneck section is expressed as:
MCTM、DMCTM及BMCTM可表示混合交通流环境下正常路段、
动态限速路段及瓶颈路段的交通流的时空演变过程，即可用来预测未来时刻不同限速方案的交通流参数。MCTM, DMCTM and BMCTM can represent normal road sections, The spatiotemporal evolution process of traffic flow in dynamic speed limit sections and bottleneck sections can be used to predict the traffic flow parameters of different speed limit schemes in the future.
进一步的，在步骤S6中，所述根据改进的元胞传输模型选出最优限速值，将最优限速值通过动态限速控制系统进行发布，包括：Further, in step S6, the optimal speed limit value is selected based on the improved cellular transmission model, and the optimal speed limit value is released through the dynamic speed limit control system, including:
(1)确定目标函数(1) Determine the objective function
主要目标为高速公路瓶颈路段行车安全与通行效率，安全方面，使用相邻元胞之间同一时刻的速度差之和的最小化为行车安全目标：
The main goals are driving safety and traffic efficiency in the bottleneck section of the expressway. In terms of safety, minimizing the sum of the speed differences between adjacent cells at the same time is used as the driving safety goal:
The main goals are driving safety and traffic efficiency in the bottleneck section of the expressway. In terms of safety, minimizing the sum of the speed differences between adjacent cells at the same time is used as the driving safety goal:
效率方面，使用总通行交通量最大为通行效率目标：
In terms of efficiency, the maximum total traffic volume is used as the traffic efficiency target:
In terms of efficiency, the maximum total traffic volume is used as the traffic efficiency target:
采用线性加权的方法将多目标问题转化为单目标问题，为了消除评价指标值量纲不统一对结果的影响，分别对两个评价指标值进行归一化处理，将处理后的评价指标进行加权组合成，目标函数：
The linear weighting method is used to convert the multi-objective problem into a single-objective problem. In order to eliminate the impact of non-uniform evaluation index value dimensions on the results, the two evaluation index values are normalized respectively, and the processed evaluation indexes are weighted. Combined into the objective function:
The linear weighting method is used to convert the multi-objective problem into a single-objective problem. In order to eliminate the impact of non-uniform evaluation index value dimensions on the results, the two evaluation index values are normalized respectively, and the processed evaluation indexes are weighted. Combined into the objective function:
其中，α1，α2分别为两个目标的权值，α1+α2＝1；分别为两个目标归一化后的取值。Among them, α 1 and α 2 are the weights of the two targets respectively, α 1 +α 2 =1; are the normalized values of the two targets respectively.
(2)确定约束条件(2) Determine the constraints
(2.1)最大化、最小化约束，限速元胞的最大限速不能大于路
段的最大安全车速，一般为路段的最大限速值120km/h或100km/h，最小限速不能小于最小通行效率所对应的最小通行速度，一般为20km/h；(2.1) Maximization and minimization constraints, the maximum speed limit of the speed limit cell cannot be greater than the road The maximum safe speed of the road section is generally the maximum speed limit value of 120km/h or 100km/h. The minimum speed limit cannot be less than the minimum traffic speed corresponding to the minimum traffic efficiency, which is generally 20km/h;
(2.2)时间变化约束，为了减少速度变化对减小速度变化对驾驶操作和车流稳定性带来的不利影响，同一个元胞的限速值相邻控制周期的变化不能超过10km/h；(2.2) Time change constraint: In order to reduce the adverse effects of speed changes on driving operation and traffic flow stability, the speed limit value of the same cell cannot change in adjacent control periods by more than 10km/h;
(2.3)空间变化约束，为了使速度平滑下降，减少速度剧烈变化带来的安全隐患，相邻两元胞之间限速值之差小于20km/h，上游元胞限速值需大于或等于下游元胞；(2.3) Spatial change constraints. In order to make the speed decrease smoothly and reduce the safety hazards caused by drastic changes in speed, the difference in speed limit value between two adjacent cells must be less than 20km/h, and the speed limit value of the upstream cell must be greater than or equal to downstream cells;
(2.4)显示方便约束，限速值为10的倍数；(2.4) Display convenience constraints, the speed limit value is a multiple of 10;
(3)求解优化模型(3) Solve the optimization model
根据优化模型目标函数及约束条件，对限速控制模型进行求解，其实质是找到各个限速元胞在控制周期最优限速值，使得总目标函数达到最佳；According to the optimization model objective function and constraint conditions, the speed limit control model is solved. The essence is to find the optimal speed limit value of each speed limit cell in the control period, so that the overall objective function can be optimal;
(4)下达限速方案(4) Issue a speed limit plan
将最优限速值通过动态限速控制系统进行发布，时间持续一个控制周期。The optimal speed limit value is released through the dynamic speed limit control system, and the time lasts for one control cycle.
进一步的，上述动态限速控制系统包括检测设备、后台中心计算机、电子情报板、自动驾驶车辆OBU和第三方导航软件。Furthermore, the above-mentioned dynamic speed limit control system includes detection equipment, background center computer, electronic information board, autonomous vehicle OBU and third-party navigation software.
由于采用上述技术方案，本发明具有以下有益效果：Due to the adoption of the above technical solutions, the present invention has the following beneficial effects:
本发明在人工-自动驾驶混合交通流环境下，基于实时检测获取
的高速公路交通流数据，运用改进的元胞传输模型及动态限速优化算法，通过路侧可变限速板、自动驾驶车载终端及第三方出行软件实时显示当前限速值，实现了高速公路瓶颈区域动态控制车辆的行驶速度，从而提高车辆的行车安全及通行效率，缓解交通拥堵。This invention obtains data based on real-time detection in a mixed traffic flow environment of manual and automatic driving. Highway traffic flow data, using improved cellular transmission model and dynamic speed limit optimization algorithm, real-time display of the current speed limit value through roadside variable speed limit boards, autonomous driving vehicle terminals and third-party travel software, realizing highway The bottleneck area dynamically controls the driving speed of vehicles, thereby improving vehicle driving safety and traffic efficiency and alleviating traffic congestion.
本发明有效实现了对人工-自动驾驶混合交通流环境下的动态速度控制，防止限速值突变，造成交通事故的发生，提高了混合交通流环境下高速公路瓶颈区域的安全性和便捷性。The invention effectively realizes dynamic speed control in a manual-automatic driving mixed traffic flow environment, prevents sudden changes in speed limit value and causes traffic accidents, and improves the safety and convenience of highway bottleneck areas in a mixed traffic flow environment.
图1为本发明的控制流程图；Figure 1 is a control flow chart of the present invention;
图2为本发明的高速公路控制路段划分示意图；Figure 2 is a schematic diagram of the highway control section division according to the present invention;
下面结合附图及实施例对本发明的技术方案作进一步详细的说明。The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and examples.
一种基于改进的元胞传输模型的混合交通流环境高速公路瓶颈路段动态限速控制方法，包括以下步骤：A dynamic speed limit control method for bottleneck sections of expressways in mixed traffic flow environments based on an improved cellular transmission model, including the following steps:
S1、在人工-自动驾驶混合交通流环境下，利用交通事件检测设备或施工作业上报系统对高速公路瓶颈路段进行识别；S1. In a mixed traffic flow environment of manual and autonomous driving, use traffic incident detection equipment or construction operation reporting system to identify highway bottleneck sections;
S2、识别完成后，设置限速控制周期及模型预测周期均为5分钟(限速值5分钟更新一次，模型预测下一个时刻的长度为5分钟)；S2. After the identification is completed, set the speed limit control period and the model prediction period to 5 minutes (the speed limit value is updated every 5 minutes, and the length of the next moment predicted by the model is 5 minutes);
S3、根据瓶颈路段所在区域划分控制路段，将瓶颈路段上游500m记为缓冲路段；将缓冲路段上游20km设置为限速路段，限速路段分段
间隔5km，划分示意如图2所示；S3. Divide the control section according to the area where the bottleneck section is located. Record the 500m upstream of the bottleneck section as a buffer section; set the 20km upstream of the buffer section as a speed limit section, and divide the speed limit section into sections. The intervals are 5km, and the division diagram is shown in Figure 2;
S4、利用交通流监测设备对高速公路待控制路段的交通流数据进行采集，包括人工、自动驾驶车辆比例、车道占用情况、速度、流量、密度和当前限速值，采集频率为5分钟一次；S4. Use traffic flow monitoring equipment to collect traffic flow data on the section of the highway to be controlled, including the proportion of manual and autonomous vehicles, lane occupancy, speed, flow, density and current speed limit value. The collection frequency is once every 5 minutes;
S5、根据采集的交通流数据和混合交通流环境下高速公路正常、限速及瓶颈路段的交通流特性对元胞传输模型进行优化，得到改进元胞传输模型，基于采集的实时交通流数据，预测混合交通流环境下各限速方案控制下下一周期内的交通流数据：S5. Optimize the cellular transmission model based on the collected traffic flow data and the traffic flow characteristics of normal, speed limit and bottleneck sections of the highway in a mixed traffic flow environment, and obtain an improved cellular transmission model. Based on the collected real-time traffic flow data, Predict the traffic flow data in the next period under the control of each speed limit scheme in a mixed traffic flow environment:
(1)搭建人工-自动驾驶混合交通流元胞传输模型，即MCTM模型(1) Build a manual-autonomous driving hybrid traffic flow cell transmission model, namely the MCTM model
人工驾驶跟驰模型中，车头间距sr与平均车速v之间的关系式为：
sr＝trv+dr In the manual driving car-following model, the relationship between the head-to-head distance s r and the average vehicle speed v is:
s r =t r v + d r
sr＝trv+dr In the manual driving car-following model, the relationship between the head-to-head distance s r and the average vehicle speed v is:
s r =t r v + d r
tr为人工驾驶反应延时；dr为人工驾驶位移差项，根据相关研究对Newell人工驾驶跟驰模型的标定，tr＝1.61s，dr＝8.53m；t r is the manual driving reaction delay; d r is the manual driving displacement difference term. According to the calibration of the Newell manual driving car-following model based on relevant research, t r =1.61s, d r =8.53m;
自动驾驶跟驰模型中，ta为自动驾驶期望车头时距；da为自动驾驶拥挤状态车头间距，交通流约束关系为：
sa＝tav+da In the automatic driving car-following model, t a is the expected headway of automatic driving; d a is the headway of automatic driving in crowded conditions. The traffic flow constraint relationship is:
s a =t a v+d a
sa＝tav+da In the automatic driving car-following model, t a is the expected headway of automatic driving; d a is the headway of automatic driving in crowded conditions. The traffic flow constraint relationship is:
s a =t a v+d a
根据PATH实验室自动驾驶车辆跟驰模型，参数标定结果为ta＝0.6s，da＝7m；According to the PATH laboratory autonomous vehicle car-following model, the parameter calibration results are t a =0.6s, d a =7m;
人工驾驶和自动驾驶混合状态下的交通流可通过平均所有车辆车头间距的方式来分析不同自动驾驶车辆比例下的混合交通流基本图特性，使用p＝(p1,p2)分别为两类交通流的比例，p1+p2＝1，各
类车型比例为p时，阻塞密度表示为：
Traffic flow in a mixed state of manual driving and automatic driving can be analyzed by averaging the distance between all vehicles to analyze the basic diagram characteristics of mixed traffic flow under different proportions of automatic driving vehicles. Use p = (p 1 , p 2 ) to divide the two categories respectively. The proportion of traffic flow, p 1 + p 2 = 1, each When the proportion of car-like vehicles is p, the blocking density is expressed as:
Traffic flow in a mixed state of manual driving and automatic driving can be analyzed by averaging the distance between all vehicles to analyze the basic diagram characteristics of mixed traffic flow under different proportions of automatic driving vehicles. Use p = (p 1 , p 2 ) to divide the two categories respectively. The proportion of traffic flow, p 1 + p 2 = 1, each When the proportion of car-like vehicles is p, the blocking density is expressed as:
临界密度为：
The critical density is:
The critical density is:
反向波速为：
The reverse wave speed is:
The reverse wave speed is:
最大通行能力为：
The maximum traffic capacity is:
The maximum traffic capacity is:
平均速度可表示为：
The average speed can be expressed as:
The average speed can be expressed as:
使用λm表示车道数，流出速率可表示为：
qm,i(k)＝ρm,i(k)vm,i(k)λm Using λ m to represent the number of lanes, the outflow rate can be expressed as:
q m,i (k)＝ρ m,i (k)v m,i (k)λ m
qm,i(k)＝ρm,i(k)vm,i(k)λm Using λ m to represent the number of lanes, the outflow rate can be expressed as:
q m,i (k)＝ρ m,i (k)v m,i (k)λ m
元胞i的车辆数可表示为：
nm,i(k+1)＝nm,i(k)+ym,i-1(k)-ym,i(k)The number of vehicles in cell i can be expressed as:
n m,i (k+1)＝n m,i (k)+y m,i-1 (k)-y m,i (k)
nm,i(k+1)＝nm,i(k)+ym,i-1(k)-ym,i(k)The number of vehicles in cell i can be expressed as:
n m,i (k+1)＝n m,i (k)+y m,i-1 (k)-y m,i (k)
其中，nm,i(k)为元胞内原有车辆数，ym,i-1(k)为流入车辆数、ym,i(k)为流出车辆数，驾驶该路段无流入流出匝道，流入量和流出量计算公式为：
ym,i(k)＝qm,i(k)ΔtAmong them, n m,i (k) is the original number of vehicles in the cell, y m,i-1 (k) is the number of incoming vehicles, y m,i (k) is the number of outgoing vehicles, and there are no incoming and outgoing ramps on this road section. , the calculation formula for inflow and outflow is:
y m,i (k)＝q m,i (k)Δt
ym,i(k)＝qm,i(k)ΔtAmong them, n m,i (k) is the original number of vehicles in the cell, y m,i-1 (k) is the number of incoming vehicles, y m,i (k) is the number of outgoing vehicles, and there are no incoming and outgoing ramps on this road section. , the calculation formula for inflow and outflow is:
y m,i (k)＝q m,i (k)Δt
根据交通流理论，元胞i的车辆数也可表示为：
nm,i(k+1)＝ρm,i(k+1)Lmλm According to traffic flow theory, the number of vehicles in cell i can also be expressed as:
n m,i (k+1)＝ρ m,i (k+1)L m λ m
nm,i(k+1)＝ρm,i(k+1)Lmλm According to traffic flow theory, the number of vehicles in cell i can also be expressed as:
n m,i (k+1)＝ρ m,i (k+1)L m λ m
元胞i在k+1时刻的密度可表示为：
The density of cell i at time k+1 can be expressed as:
The density of cell i at time k+1 can be expressed as:
(2)搭建动态限速MCTM模型，即DMCTM模型(2) Build a dynamic speed limit MCTM model, that is, the DMCTM model
根据DMCTM，使用vdsl表示动态限速值，平均速度可表示为：
According to DMCTM, v dsl is used to represent the dynamic speed limit value, and the average speed can be expressed as:
According to DMCTM, v dsl is used to represent the dynamic speed limit value, and the average speed can be expressed as:
最大通行能力与临界密度之间的关系可表示为：
maximum traffic capacity with critical density The relationship between them can be expressed as:
maximum traffic capacity with critical density The relationship between them can be expressed as:
流入元胞的流率可表示为：
The flow rate into the cell can be expressed as:
The flow rate into the cell can be expressed as:
密度可根据车辆数守恒推导获得，与MCTM一致。The density can be derived from the conservation of vehicle number, which is consistent with MCTM.
(3)搭建瓶颈路段MCTM模型，即BMCTM模型(3) Build the MCTM model of the bottleneck section, that is, the BMCTM model
根据BMCTM，瓶颈路段的流出量：
According to BMCTM, the outflow volume of the bottleneck section is:
According to BMCTM, the outflow volume of the bottleneck section is:
其中，γ为通行能力下降幅度，根据交通流理论，瓶颈路段的交通流速度vm,i(k)可表示为：
in, γ is the decrease in traffic capacity. According to the traffic flow theory, the traffic flow speed v m,i (k) of the bottleneck section can be expressed as:
in, γ is the decrease in traffic capacity. According to the traffic flow theory, the traffic flow speed v m,i (k) of the bottleneck section can be expressed as:
MCTM、DMCTM及BMCTM可表示混合交通流环境下正常路段、动态限速路段及瓶颈路段的交通流的时空演变过程，根据上述改进的元胞传输模型及采集的实时交通流数据，即可预测未来时刻不同限速方案的交通流参数；MCTM, DMCTM and BMCTM can represent the spatio-temporal evolution process of traffic flow in normal sections, dynamic speed limit sections and bottleneck sections in a mixed traffic flow environment. Based on the above-mentioned improved cellular transmission model and the collected real-time traffic flow data, the future can be predicted Traffic flow parameters for different speed limit schemes at all times;
S6、根据改进的元胞传输模型选出最优限速值，将最优限速值通过动态限速控制系统进行发布，包括：S6. Select the optimal speed limit value based on the improved cellular transmission model, and publish the optimal speed limit value through the dynamic speed limit control system, including:
(1)确定目标函数(1) Determine the objective function
主要目标为高速公路瓶颈路段行车安全与通行效率，安全方面，使用相邻元胞之间同一时刻的速度差之和的最小化为行车安全目标：
The main goals are driving safety and traffic efficiency in the bottleneck section of the expressway. In terms of safety, minimizing the sum of the speed differences between adjacent cells at the same time is used as the driving safety goal:
The main goals are driving safety and traffic efficiency in the bottleneck section of the expressway. In terms of safety, minimizing the sum of the speed differences between adjacent cells at the same time is used as the driving safety goal:
效率方面，使用总通行交通量最大为通行效率目标：
In terms of efficiency, the maximum total traffic volume is used as the traffic efficiency target:
In terms of efficiency, the maximum total traffic volume is used as the traffic efficiency target:
采用线性加权的方法将多目标问题转化为单目标问题；为了消除评价指标值量纲不统一对结果的影响，分别对两个评价指标值进行归一化处理，将处理后的评价指标进行加权组合成，目标函数：
The linear weighting method is used to convert the multi-objective problem into a single-objective problem; in order to eliminate the impact of the non-uniform dimensions of the evaluation index values on the results, the two evaluation index values are normalized respectively, and the processed evaluation indexes are weighted. Combined into the objective function:
The linear weighting method is used to convert the multi-objective problem into a single-objective problem; in order to eliminate the impact of the non-uniform dimensions of the evaluation index values on the results, the two evaluation index values are normalized respectively, and the processed evaluation indexes are weighted. Combined into the objective function:
其中，α1，α2分别为两个目标的权值，α1+α2＝1；分别为两个目标归一化后的取值；Among them, α 1 and α 2 are the weights of the two targets respectively, α 1 +α 2 =1; are the normalized values of the two targets respectively;
(2)确定约束条件(2) Determine the constraints
在实际应用中，需要添加一些约束条件，保证动态限速策略的顺利实施；In practical applications, some constraints need to be added to ensure the smooth implementation of the dynamic speed limit strategy;
(2.1)最大化、最小化约束；限速元胞的最大限速不能大于路段的最大安全车速，一般为路段的最大限速值120km/h或100km/h。最小限速不能小于最小通行效率所对应的最小通行速度，一般为20km/h；(2.1) Maximization and minimization constraints; the maximum speed limit of the speed limit cell cannot be greater than the maximum safe speed of the road section, which is generally the maximum speed limit of the road section 120km/h or 100km/h. The minimum speed limit cannot be less than the minimum traffic speed corresponding to the minimum traffic efficiency, which is generally 20km/h;
(2.2)时间变化约束，为了减少速度变化对减小速度变化对驾驶操作和车流稳定性带来的不利影响，同一个元胞的限速值相邻控制周期的变化不能超过10km/h；(2.2) Time change constraint: In order to reduce the adverse effects of speed changes on driving operation and traffic flow stability, the speed limit value of the same cell cannot change in adjacent control periods by more than 10km/h;
(2.3)空间变化约束，为了使速度平滑下降，减少速度剧烈变化带来的安全隐患，相邻两元胞之间限速值之差小于20km/h，上游元胞限速值需大于或等于下游元胞；(2.3) Spatial change constraints. In order to make the speed decrease smoothly and reduce the safety hazards caused by drastic changes in speed, the difference in speed limit value between two adjacent cells must be less than 20km/h, and the speed limit value of the upstream cell must be greater than or equal to downstream cells;
(2.4)显示方便约束，限速值为10的倍数；(2.4) Display convenience constraints, the speed limit value is a multiple of 10;
(3)求解优化模型(3) Solve the optimization model
根据优化模型目标函数及约束条件，对限速控制模型进行求解，其实质是找到各个限速元胞在控制周期最优限速值，使得总目标函数达到最佳，可采用遗传算法求解；According to the optimization model objective function and constraint conditions, the speed limit control model is solved. The essence is to find the optimal speed limit value of each speed limit cell in the control period, so that the overall objective function reaches the optimum, which can be solved by genetic algorithm;
(4)下达限速方案(4) Issue a speed limit plan
将最优限速值通过动态限速控制系统进行发布，时间持续一个控制周
期；The optimal speed limit value is released through the dynamic speed limit control system for a control week. Expect;
S7、重复S4、S5、S6步骤再次确定最优限速值，实现混合交通流环境高速公路瓶颈路段动态限速控制。S7. Repeat steps S4, S5, and S6 to determine the optimal speed limit value again to achieve dynamic speed limit control on the bottleneck section of the expressway in a mixed traffic flow environment.
本发明未详述部分为现有技术，尽管结合优选实施方案具体展示和介绍了本发明，具体实现该技术方案方法和途径很多，以上所述仅是本发明的优选实施方式，但所属领域的技术人员应该明白，在不脱离所附权利要求书所限定的本发明的精神和范围内，在形式上和细节上可以对本发明做出各种变化，均为本发明的保护范围。
The parts not described in detail in the present invention are prior art. Although the present invention has been specifically demonstrated and introduced in combination with the preferred embodiments, there are many methods and approaches to implement the technical solutions. The above are only the preferred embodiments of the present invention, but there are many limitations in the field. It should be understood by those skilled in the art that various changes can be made in the form and details of the present invention without departing from the spirit and scope of the present invention as defined by the appended claims, and all of them fall within the protection scope of the present invention.
Claims (4)
- 一种混合交通流环境高速公路瓶颈路段动态限速控制方法，其特征在于，包括以下步骤：A dynamic speed limit control method for bottleneck sections of highways in mixed traffic flow environments, which is characterized by including the following steps:S1、在人工-自动驾驶混合交通流环境下，利用交通事件检测设备或施工作业上报系统对高速公路瓶颈路段进行识别；S1. In a mixed traffic flow environment of manual and autonomous driving, use traffic incident detection equipment or construction operation reporting system to identify highway bottleneck sections;S2、识别完成后，设置限速控制周期及模型预测周期，模型预测周期为限速控制周期的整数倍，限速控制周期为5-10min；S2. After the identification is completed, set the speed limit control period and model prediction period. The model prediction period is an integer multiple of the speed limit control period, and the speed limit control period is 5-10 minutes;S3、根据瓶颈路段所在区域划分控制路段，将瓶颈路段上游500-700m设置为缓冲路段；将缓冲路段上游5-20km设置为限速路段，限速路段分段间隔1-5km；S3. Divide control sections according to the area where the bottleneck section is located, and set 500-700m upstream of the bottleneck section as a buffer section; set 5-20km upstream of the buffer section as a speed-limited section, with intervals of 1-5km between speed-limit sections;S4、利用交通流监测设备对高速公路待控制路段的交通流数据进行采集，包括人工、自动驾驶车辆比例、车道占用情况、速度、流量、密度和当前限速值，采集频率根据模型控制周期设置，需小于或等于模型控制周期；S4. Use traffic flow monitoring equipment to collect traffic flow data on the section of the highway to be controlled, including the proportion of manual and autonomous vehicles, lane occupancy, speed, flow, density and current speed limit value. The collection frequency is set according to the model control cycle , needs to be less than or equal to the model control period;S5、根据采集的交通流数据和混合交通流环境下高速公路正常、限速及瓶颈路段的交通流特性对元胞传输模型进行优化，得到改进元胞传输模型，基于采集的实时交通流数据，预测混合交通流环境下各限速方案控制下下一周期内的交通流数据；S5. Optimize the cellular transmission model based on the collected traffic flow data and the traffic flow characteristics of normal, speed limit and bottleneck sections of the highway in a mixed traffic flow environment, and obtain an improved cellular transmission model. Based on the collected real-time traffic flow data, Predict the traffic flow data in the next period under the control of each speed limit scheme in a mixed traffic flow environment;S6、根据改进的元胞传输模型选出最优限速值，将最优限速值通过动态限速控制系统进行发布；S6. Select the optimal speed limit value based on the improved cellular transmission model, and publish the optimal speed limit value through the dynamic speed limit control system;S7、重复S4、S5、S6步再次确定最优限速值，实现混合交通流环境高速公路瓶颈路段动态限速控制。S7. Repeat steps S4, S5, and S6 to determine the optimal speed limit value again to achieve dynamic speed limit control on the bottleneck section of the expressway in a mixed traffic flow environment.
- 根据权利要求1所述混合交通流环境高速公路瓶颈路段动态限 速控制方法，其特征在于，在步骤S5中，所述根据采集的交通流数据和混合交通流环境下高速公路正常、限速及瓶颈路段的交通流特性对元胞传输模型进行优化，得到改进元胞传输模型，包括：According to the dynamic limit of the bottleneck section of the expressway in the mixed traffic flow environment of claim 1 Speed control method, characterized in that, in step S5, the cellular transmission model is optimized and improved based on the collected traffic flow data and the traffic flow characteristics of normal, speed limit and bottleneck sections of the highway in a mixed traffic flow environment. Cellular transport models, including:根据元胞传输模型要求将瓶颈路段、缓冲路段及限速路段记为控制路段m，将m路段以单位长度Lm为单位划分为Nm个元胞；According to the requirements of the cell transmission model, bottleneck sections, buffer sections and speed limit sections are recorded as control section m, and the m section is divided into N m cells with unit length L m as the unit;(1)搭建人工-自动驾驶混合交通流元胞传输模型，即MCTM模型(1) Build a manual-autonomous driving hybrid traffic flow cell transmission model, namely the MCTM model人工驾驶跟驰模型中，车头间距sr与平均车速v之间的关系式为：
sr＝trv+dr In the manual driving car-following model, the relationship between the head-to-head distance s r and the average vehicle speed v is:
s r =t r v + d rtr为人工驾驶反应延时；dr为人工驾驶位移差项；t r is the manual driving reaction delay; d r is the manual driving displacement difference term;自动驾驶跟驰模型中，ta为自动驾驶期望车头时距；da为自动驾驶拥挤状态车头间距，交通流约束关系为：
sa＝tav+da In the automatic driving car-following model, t a is the expected headway of automatic driving; d a is the headway of automatic driving in crowded conditions. The traffic flow constraint relationship is:
s a =t a v+d a人工驾驶和自动驾驶混合状态下的交通流可通过平均所有车辆车头间距的方式来分析不同自动驾驶车辆比例下的混合交通流基本图特性，使用p＝(p1,p2)分别为两类交通流的比例，p1+p2＝1，各类车型比例为p时，阻塞密度表示为：
Traffic flow in a mixed state of manual driving and automatic driving can be analyzed by averaging the distance between all vehicles to analyze the basic diagram characteristics of mixed traffic flow under different proportions of automatic driving vehicles. Use p = (p 1 , p 2 ) to divide the two categories respectively. The proportion of traffic flow, p 1 + p 2 = 1, and the proportion of various vehicle types is p, the congestion density is expressed as:
临界密度表示为：
The critical density is expressed as:
反向波速表示为：
The reverse wave speed is expressed as:
最大通行能力表示为：
The maximum traffic capacity is expressed as:
元胞传输模型需要对交通流进行时空离散化处理，将m路段以单位长度Lm为单位划分为Nm个元胞，在混合交通流情况下，元胞i∈{1,2,…,Nm}在第k∈{0,1,2,…,K}个时间间隔(时间间隔为Δt)的交通流参数包括：平均速度vm,i(k)、元胞驶入流率qm,i-1(k)、驶出流率qm,i(k)、车流密度ρm,i(k)，根据元胞传输模型，平均速度可表示为：
The cellular transmission model requires spatio-temporal discretization of the traffic flow. The m road section is divided into N m cells with unit length L m . In the case of mixed traffic flow, the cell i∈{1,2,…, N m } The traffic flow parameters at the k∈{0,1,2,…,K}th time interval (the time interval is Δt) include: average speed v m,i (k), cell entry flow rate q m,i-1 (k), exit flow rate q m,i (k), traffic flow density ρ m,i (k), according to the cell transmission model, the average speed can be expressed as:
使用λm表示车道数，流出速率可表示为：
qm,i(k)＝ρm,i(k)vm,i(k)λm Using λ m to represent the number of lanes, the outflow rate can be expressed as:
q m,i (k)＝ρ m,i (k)v m,i (k)λ m元胞i的车辆数可表示为：
nm,i(k+1)＝nm,i(k)+ym,i-1(k)-ym,i(k)The number of vehicles in cell i can be expressed as:
n m,i (k+1)＝n m,i (k)+y m,i-1 (k)-y m,i (k)其中，nm,i(k)为元胞内原有车辆数，ym,i-1(k)为流入车辆数、ym,i(k)为流出车辆数，驾驶该路段无流入流出匝道，流入量和流出量计算公式为：
ym,i(k)＝qm,i(k)ΔtAmong them, n m,i (k) is the original number of vehicles in the cell, y m,i-1 (k) is the number of incoming vehicles, y m,i (k) is the number of outgoing vehicles, and there are no incoming and outgoing ramps on this road section. , the calculation formula for inflow and outflow is:
y m,i (k)＝q m,i (k)Δt根据交通流理论，元胞i的车辆数也可表示为：
nm,i(k+1)＝ρm,i(k+1)Lmλm According to traffic flow theory, the number of vehicles in cell i can also be expressed as:
n m,i (k+1)＝ρ m,i (k+1)L m λ m元胞i在k+1时刻的密度可表示为：
The density of cell i at time k+1 can be expressed as:
根据上述公式推导，可计算出不同人工和自动驾驶车辆比例情况下，混合交通流当前元胞在下一时刻的车辆数、密度等交通流参数，再遍历整个元胞层与时间层，可计算混合交通流的时空交通状态演化，完成MCTM建模；According to the above formula derivation, the traffic flow parameters such as the number and density of vehicles in the current cell of the mixed traffic flow at the next moment can be calculated under different proportions of manual and autonomous vehicles. Then the entire cell layer and time layer can be traversed to calculate the mixed traffic flow parameters. The spatio-temporal traffic state evolution of traffic flow is completed to complete MCTM modeling;(2)搭建动态限速MCTM模型，即DMCTM模型(2) Build a dynamic speed limit MCTM model, that is, the DMCTM model使用vdsl表示动态限速值，平均速度可表示为：
Using vdsl to represent the dynamic speed limit value, the average speed can be expressed as:
动态限速条件下,最大通行能力与临界密度之间的关系可表示为：
Maximum traffic capacity under dynamic speed limit conditions with critical density The relationship between them can be expressed as:
流入元胞的流率可表示为：
The flow rate into the cell can be expressed as:
密度根据车辆数守恒推导获得，根据上述DMCTM模型公式可递推得到任一时空交通流基本参数；The density is derived based on the conservation of the number of vehicles. According to the above DMCTM model formula, the basic parameters of any space-time traffic flow can be obtained recursively;(3)搭建瓶颈路段MCTM模型，即BMCTM模型(3) Build the MCTM model of the bottleneck section, that is, the BMCTM model瓶颈路段的流出量：
Outflow volume at the bottleneck section:
其中，γ为通行能力下降幅度，根据交通流理论，瓶颈路段的交通流速度vm,i(k)表示为：
in, γ is the decrease in traffic capacity. According to the traffic flow theory, the traffic flow speed v m,i (k) of the bottleneck section is expressed as:
MCTM、DMCTM及BMCTM可表示混合交通流环境下正常路段、动态限速路段及瓶颈路段的交通流的时空演变过程，即可用来预测未来时刻不同限速方案的交通流参数。MCTM, DMCTM and BMCTM can represent the spatio-temporal evolution process of traffic flow in normal sections, dynamic speed limit sections and bottleneck sections in a mixed traffic flow environment, and can be used to predict the traffic flow parameters of different speed limit schemes in the future. - 根据权利要求1所述混合交通流环境高速公路瓶颈路段动态限速控制方法，其特征在于，所述步骤S6中，所述根据改进的元胞传输模型选出最优限速值，将最优限速值通过动态限速控制系统进行发布，包括：The dynamic speed limit control method for highway bottleneck sections in a mixed traffic flow environment according to claim 1, characterized in that in step S6, the optimal speed limit value is selected according to the improved cellular transmission model, and the optimal speed limit value is Speed limit values are issued through the dynamic speed limit control system, including:(1)确定目标函数(1) Determine the objective function主要目标为高速公路瓶颈路段行车安全与通行效率，安全方面，使用相邻元胞之间同一时刻的速度差之和的最小化为行车安全目标：
The main goals are driving safety and traffic efficiency in the bottleneck section of the expressway. In terms of safety, minimizing the sum of the speed differences between adjacent cells at the same time is used as the driving safety goal:
效率方面，使用总通行交通量最大为通行效率目标：
In terms of efficiency, the maximum total traffic volume is used as the traffic efficiency target:
采用线性加权的方法将多目标问题转化为单目标问题，为了消除评价指标值量纲不统一对结果的影响，分别对两个评价指标值进行归 一化处理，将处理后的评价指标进行加权组合成，目标函数：
The linear weighting method is used to convert the multi-objective problem into a single-objective problem. In order to eliminate the impact of the non-uniform dimensions of the evaluation index values on the results, the two evaluation index values are normalized respectively. Unified processing, the processed evaluation indicators are weighted and combined into an objective function:
其中，α1，α2分别为两个目标的权值，α1+α2＝1；分别为两个目标归一化后的取值；Among them, α 1 and α 2 are the weights of the two targets respectively, α 1 +α 2 =1; are the normalized values of the two targets respectively;(2)确定约束条件(2) Determine the constraints(2.1)最大化、最小化约束，限速元胞的最大限速不能大于路段的最大安全车速，一般为路段的最大限速值120km/h或100km/h，最小限速不能小于最小通行效率所对应的最小通行速度，一般为20km/h；(2.1) Maximization and minimization constraints. The maximum speed limit of the speed limit cell cannot be greater than the maximum safe speed of the road section, which is generally the maximum speed limit of the road section 120km/h or 100km/h. The minimum speed limit cannot be less than the minimum traffic efficiency. The corresponding minimum traffic speed is generally 20km/h;(2.2)时间变化约束，为了减少速度变化对减小速度变化对驾驶操作和车流稳定性带来的不利影响，同一个元胞的限速值相邻控制周期的变化不能超过10km/h；(2.2) Time change constraint: In order to reduce the adverse effects of speed changes on driving operation and traffic flow stability, the speed limit value of the same cell cannot change in adjacent control periods by more than 10km/h;(2.3)空间变化约束，为了使速度平滑下降，减少速度剧烈变化带来的安全隐患，相邻两元胞之间限速值之差小于20km/h，上游元胞限速值需大于或等于下游元胞；(2.3) Spatial change constraints. In order to make the speed decrease smoothly and reduce the safety hazards caused by drastic changes in speed, the difference in speed limit value between two adjacent cells must be less than 20km/h, and the speed limit value of the upstream cell must be greater than or equal to downstream cells;(2.4)显示方便约束，限速值为10的倍数；(2.4) Display convenience constraints, the speed limit value is a multiple of 10;(3)求解优化模型(3) Solve the optimization model根据优化模型目标函数及约束条件，对限速控制模型进行求解，其实质是找到各个限速元胞在控制周期最优限速值，使得总目标函数达到最佳；According to the optimization model objective function and constraint conditions, the speed limit control model is solved. The essence is to find the optimal speed limit value of each speed limit cell in the control period, so that the overall objective function can be optimal;(4)下达限速方案(4) Issue a speed limit plan将最优限速值通过动态限速控制系统进行发布，时间持续一个控制周期。 The optimal speed limit value is released through the dynamic speed limit control system, and the time lasts for one control cycle. - 根据权利要求1所述混合交通流环境高速公路瓶颈路段动态限速控制方法，其特征在于：所述动态限速控制系统包括检测设备、后台中心计算机、电子情报板、自动驾驶车辆OBU和第三方导航软件。 The method for dynamic speed limit control on a bottleneck section of a highway in a mixed traffic flow environment according to claim 1, characterized in that: the dynamic speed limit control system includes a detection device, a backend central computer, an electronic information board, an autonomous vehicle OBU and a third party Navigation software.
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