WO2022179321A1 - 一种道路检测设备布设方法、装置及存储介质 - Google Patents

一种道路检测设备布设方法、装置及存储介质 Download PDF

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WO2022179321A1
WO2022179321A1 PCT/CN2022/070994 CN2022070994W WO2022179321A1 WO 2022179321 A1 WO2022179321 A1 WO 2022179321A1 CN 2022070994 W CN2022070994 W CN 2022070994W WO 2022179321 A1 WO2022179321 A1 WO 2022179321A1
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road
detection device
optimization model
detection equipment
path
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PCT/CN2022/070994
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English (en)
French (fr)
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张晓春
林涛
邹莉
陈振武
周勇
周子益
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深圳市城市交通规划设计研究中心股份有限公司
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Publication of WO2022179321A1 publication Critical patent/WO2022179321A1/zh

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the present invention relates to the technical field of road detection, and in particular, to a method, device and storage medium for laying out road detection equipment.
  • the regional traffic signal control system refers to a system that takes all the signal-controlled intersections of the urban road network as a whole and links them to optimize the control of traffic signals. Traffic safety and improving traffic operation efficiency.
  • Regional traffic signal control adopts multi-path traffic flow coordination, inflow and outflow control strategies of bottleneck sections, etc. to achieve effective linkage of the entire region, not only the flow of a single intersection, but also the traffic flow OD (Origin-Destination, traffic travel volume of the entire road network) ) route demand traffic, etc.
  • traffic flow OD Oil-Destination, traffic travel volume of the entire road network
  • a 0-1 linear programming model (BILP, Binary Integer Linear Programming) is established, and the location of the detection equipment is used as the decision variable to cover the
  • the maximum OD amount of the road network is the optimization goal.
  • the optimal layout of the cross-section flow detection equipment is obtained.
  • this method needs to obtain accurate prior OD information in advance, which increases the workload, and the detection equipment deployed according to this method is only suitable for detecting the demand flow of the OD path of the traffic flow.
  • the other is to deploy vehicle identity awareness detection equipment with traffic travel path reconstruction as the main goal, establish a 0-1 linear programming model, and aim at the minimum number of equipment when all OD paths are covered by detection equipment, or at a certain Under the constraints of the number of devices or the budget, aiming at the maximum number of OD paths covered by the detection device, the location of the vehicle identity awareness detection device is solved.
  • this method does not rely on the accurate prior OD ratio, it is based on the The detection device is only suitable for detecting the demand flow of the OD path of the traffic flow.
  • the problem solved by the present invention is that the data collected by the detection equipment deployed in the prior art only includes the OD path flow, which is not comprehensive enough, and can only be optimized for the layout of a single type of detection equipment.
  • the present invention provides a method, device and storage medium for laying out road detection equipment.
  • the present invention provides a method for laying out road detection equipment, including:
  • the traffic network diagram includes OD pairs, OD paths, signal-controlled intersections, road sections and turning lanes, which are used to describe signal-controlled intersections and road sections.
  • the relationship between the steering lane and the OD path, any two nodes in the traffic network diagram are a described OD pair, the path between the OD pairs is the OD path, and the basic road information includes the The sum of the traffic at the signal control intersection, the prior traffic of the OD pair and the prior traffic of the OD path;
  • an optimization model of the layout points of the multi-type detection equipment is constructed, wherein the objective function of the optimization model is determined by the prior flow of the OD pair, the prior of the OD path
  • the sum of the inspection flow and the flow of the signal control intersection is established and obtained, the objective function of the optimization model is positively correlated with the number of the OD paths where the detection device detects traffic, and the objective function is detected by the detection device.
  • the number of the signal-controlled intersections to the flow is positively correlated, and the constraints of the optimization model include that at least one section of each OD path is installed with the detection device, and for any of the OD paths, at least 1 each detection device is adapted to distinguish the OD path from other OD paths, and the total cost of each type of the detection device is within the calibrated equipment budget;
  • the optimization model is solved to obtain the layout points of each type of the detection equipment.
  • the detection device includes a section flow detection device and a vehicle identity perception detection device.
  • the basic information of the road includes the signal-controlled intersection, the road section, and the first connection relationship between the road section and the signal-controlled intersection in the demarcated area, and the traffic network is established according to the basic road information.
  • Figures include:
  • the traffic network graph is established with the signal-controlled intersection as a node and each of the road segments connecting the signal-controlled intersection as an edge.
  • the signal-controlled intersection includes a plurality of entrance roads and a plurality of exit roads
  • the road segment includes a plurality of lanes
  • the basic road information includes a second connection relationship between the entrance road and the lane and the The third connection relationship between the exit road and the lane
  • the establishment of a traffic network map according to the basic road information includes:
  • the lanes connected to each of the entrance roads are determined according to the second connection relationship, and the lanes connected to each of the exit roads are determined according to the third connection relationship said lane;
  • the traffic network graph is established, wherein the edges include the corresponding The driving direction of the lane.
  • any two of the nodes in the traffic network diagram form an OD pair, one of the nodes in the OD pair is the starting point, and the other node is the end point, and the route from the start point to the end point passes through.
  • the path is the OD path corresponding to the OD pair, and the basic road information includes the prior flow of the OD pair and the prior flow of the OD path; the multi-type detection device is constructed according to the traffic network map and the basic road information.
  • the optimization model of the layout point includes:
  • the prior flow of the OD path and the The sum of the traffic at the intersection is controlled by the signal, and the objective function of the optimization model is established by the first formula, and the first formula includes:
  • K 1 and K 2 are the weights of the objective function
  • w represents any of the OD pairs
  • W is the set of all the OD pairs in the traffic network diagram
  • r represents any corresponding OD pair w.
  • an OD path R w is the set of all the OD paths of the OD to w
  • n represents any one of the signal-controlled intersections
  • N S is the set of all the signal-controlled intersections in the traffic network diagram
  • q n is the sum of the flow of the signal-controlled intersection n
  • the detection equipment includes a cross-section flow detection equipment and a vehicle identity perception detection equipment
  • the optimization model for constructing the layout points of the multi-type detection equipment according to the traffic network map and the basic road information further includes:
  • a second formula is used to determine the first constraint condition of the optimization model, and the second formula includes:
  • a any edge in the traffic network graph
  • r represents any of the OD paths
  • A is the set of all edges in the traffic network graph
  • optimization model for constructing the layout points of the multi-type detection equipment according to the traffic network map and the basic road information further includes:
  • a third formula is used to determine the second constraint condition of the optimization model, and the third formula includes:
  • r1 represents any of the OD paths that share a common edge with the path r.
  • optimization model for constructing the layout points of the multi-type detection equipment according to the traffic network map and the basic road information further includes:
  • a fourth formula is used to determine the third constraint condition of the optimization model, and the fourth formula includes:
  • u a is the cost of installing the cross-section flow detection device on the side a
  • v a is the cost of installing the vehicle identity awareness detection device on the side a
  • B is the total budget.
  • optimization model for constructing the layout points of the multi-type detection equipment according to the traffic network map and the basic road information further includes:
  • a fifth formula is used to determine the fourth constraint condition of the optimization model, and the fifth formula includes:
  • AS n is the set of all edges included in the signal control intersection n and connected to each entrance road, and a1 and a2 respectively represent two different edges.
  • the solution of the optimization model to obtain the layout points of the detection equipment of each type includes:
  • the optimization model is solved to obtain the layout point of the detection device when the target value is the largest, wherein the target value is determined by the number of the OD paths of the detected traffic by the detection device and the detected traffic by the detection device.
  • the number of signal control intersections is weighted and summed.
  • the present invention provides a device for laying out road detection equipment, comprising:
  • the acquisition module is used to acquire the basic road information of the calibration area, and establish a traffic network diagram according to the basic road information, wherein the traffic network diagram includes OD pairs, OD paths, signal-controlled intersections, road sections and turning lanes, which are used to describe The signal controls the relationship between intersections, road sections, turning lanes and OD paths. Any two nodes in the traffic network diagram are one of the OD pairs, and the path between the OD pairs is the OD path, and the road
  • the basic information includes the total flow of the signal-controlled intersection, the prior flow of the OD pair and the prior flow of the OD path;
  • the building module is used to build an optimization model of the layout points of multi-type detection equipment according to the traffic network map and the basic information of the road, wherein, the objective function of the optimization model is based on the prior flow of the OD pair, all The prior flow of the OD path and the sum of the flow of the signal control intersection are established and obtained, the objective function of the optimization model is positively correlated with the number of the OD paths whose flow is detected by the detection device, and the objective function and The number of the signal-controlled intersections whose flow is detected by the detection device is positively correlated, and the constraints of the optimization model include that at least one road section on each of the OD paths is installed with the detection device, and for any of the OD paths path, at least one detection device is suitable for distinguishing the OD path from other OD paths, and the total cost of each type of the detection device is within the calibrated equipment budget;
  • the processing module is used to solve the optimization model to obtain the layout points of the detection equipment of various types.
  • the present invention provides a road detection equipment layout device, comprising a memory and a processor;
  • the memory for storing computer programs
  • the processor when executing the computer program, implements the above-mentioned method for arranging a road detection device.
  • the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned method for laying out a road detection device is implemented.
  • the beneficial effects of the road detection equipment layout method, device and storage medium of the present invention are: obtaining the basic road information of the calibration area, the basic road information may include information such as signal control intersections and road sections, and establishing a traffic network diagram according to the basic road information.
  • the graph can simplify the complex road traffic network, reduce the subsequent workload and improve the processing speed.
  • the traffic network diagram includes OD paths and signal-controlled intersections.
  • the OD path is the path from the starting point to the end point.
  • an optimization model for the layout of multiple types of detection equipment is constructed, and multiple different types of detection equipment are combined for integrated layout. , the existing detection equipment can be fully utilized, and the use of multiple types of detection equipment can reduce the cost compared with the use of a single type of detection equipment.
  • the objective function of the optimization model is positively correlated with the number of OD paths where the traffic is detected by the detection device and the number of signal-controlled intersections where the traffic is detected.
  • the optimization model is solved, and the layout points of each type of detection equipment are obtained.
  • Various types of detection equipment are arranged according to the layout point, so that under the calibrated equipment budget, more OD paths and traffic at signal control intersections can be detected, and the detection data can be improved without increasing equipment costs. comprehensiveness.
  • FIG. 1 is a schematic flowchart of a method for laying out a road detection device according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of a road traffic network corresponding to basic road information according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a signal-controlled intersection according to an embodiment of the present invention.
  • FIG 5 is another traffic network diagram according to an embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram of an apparatus for laying road detection equipment according to an embodiment of the present invention.
  • a method for laying road detection equipment provided by an embodiment of the present invention includes:
  • Step 110 Obtain the basic road information of the calibration area, and establish a traffic network diagram according to the basic road information, wherein the traffic network diagram includes OD pairs, OD paths, signal control intersections, road sections and turning lanes, which are used to describe signal control.
  • the relationship between intersections, road sections, turning lanes and OD paths, any two nodes in the traffic network diagram are an OD pair, the path between the OD pairs is the OD path, and the road basic information Including the sum of the flow of the signal control intersection, the prior flow of the OD pair and the prior flow of the OD path;
  • Step 120 Build an optimization model for the layout points of multi-type detection equipment according to the traffic network diagram and the basic information of the road, wherein the objective function of the optimization model is based on the prior flow of the OD pair, the OD The prior flow of the path and the sum of the flow of the signal-controlled intersection are established, and the objective function of the optimization model is positively related to the number of the OD paths whose flow is detected by the detection device, and the objective function is related to the The number of the signal-controlled intersections whose flow is detected by the detection device is positively correlated, and the constraints of the optimization model include that at least one road section on each of the OD paths is installed with the detection device, and for any of the OD paths, At least one detection device is adapted to distinguish the OD path from other OD paths, and the total cost of each type of the detection device is within the calibrated equipment budget.
  • the detection device includes a section flow detection device and a vehicle identity perception detection device.
  • the optimization model takes the maximum target value as the optimization goal, and the target value is obtained by the weighted summation of the number of the OD paths where the traffic is detected by the detection device and the number of the signal control intersections where the traffic is detected.
  • step 130 the optimization model is solved to obtain the layout points of each type of the detection equipment.
  • the basic road information of the calibration area is obtained.
  • the basic road information may include information such as signal-controlled intersections and road sections, and a traffic network diagram is established according to the basic road information.
  • the traffic network diagram can simplify the complex road traffic network and reduce the follow-up workload and increase processing speed.
  • the traffic network diagram includes OD paths and signal-controlled intersections.
  • the OD path is the path from the starting point to the end point.
  • an optimization model for the layout of multiple types of detection equipment is constructed, and multiple different types of detection equipment are combined for integrated layout. , the existing detection equipment can be fully utilized, and the use of multiple types of detection equipment can reduce the cost compared with the use of a single type of detection equipment.
  • the objective function of the optimization model is positively correlated with the number of OD paths where the traffic is detected by the detection device and the number of signal-controlled intersections where the traffic is detected.
  • the optimization model is solved, and the layout points of each type of detection equipment are obtained.
  • Various types of detection equipment are arranged according to the layout point, so that under the calibrated equipment budget, more OD paths and traffic at signal control intersections can be detected, and the detection data can be improved without increasing equipment costs. comprehensiveness.
  • the road traffic network shown in Figure 2 includes a total of 6 road nodes a-f, and a total of 5 one-way road segments 1-5.
  • the 6 road nodes form a ⁇ e, a ⁇ f, b ⁇ e, b ⁇ f has a total of 4 OD pairs
  • 5 one-way road sections form 1 ⁇ 3 ⁇ 4, 1 ⁇ 3 ⁇ 5, 2 ⁇ 3 ⁇ 4, 2 ⁇ 3 ⁇ 5, a total of 4 paths, among which, each OD pair corresponds to a path.
  • a cross-section flow detection device can be installed on the road section 3. Under the prior information of the known OD proportional relationship, the flow of each OD pair can be calculated according to the flow of the section 3 detected by the section flow detection device.
  • a vehicle detected on both road segment 1 and road segment 4 corresponds to OD pair a ⁇ e
  • a vehicle not detected on road segment 1 but detected on road segment 4 corresponds to OD pair b ⁇ e
  • detected on road segment 1 Vehicles that are not detected on road segment 4 correspond to OD pair a ⁇ f
  • vehicles detected on road segment 3 but not detected on road segments 1 and 4 correspond to OD pair b ⁇ f.
  • identity sensing detection equipment can be installed on road section 1 and road section 4 respectively, and cross-sectional flow detection equipment can be installed on road section 3.
  • the traffic of the OD pair For example: the vehicle detected on road segment 1 and road segment 4 corresponds to OD pair a ⁇ e, the vehicle detected on road segment 1 but not detected on road segment 4 corresponds to OD pair b ⁇ e, and detected on road segment 1
  • the undetected vehicles in section 4 correspond to the OD pair a ⁇ f, and the flow rate of the OD pair b ⁇ f can be obtained by subtracting the flow of the above three OD pairs from the total flow detected in the section 3.
  • It can be integrated according to multiple types of detection equipment such as cross-section flow detection equipment and vehicle identity awareness detection equipment, and can make full use of existing detection equipment. , can save equipment investment and reduce costs.
  • the basic road information includes the signal-controlled intersection, the road section in the calibration area, and the first connection relationship between the road section and the signal-controlled intersection.
  • the basic road information to build a traffic network diagram includes:
  • the traffic network graph is established with the signal-controlled intersection as a node and each of the road segments connecting the signal-controlled intersection as an edge.
  • the signal-controlled intersection is taken as a node. If the signal-controlled intersection is an intersection, and lanes in different driving directions of the same road section are different sides, then the intersection has eight sides connected to it, and four The edges entering the signalized junction and the four edges leaving the signalized junction.
  • the signal-controlled intersection includes multiple entry roads and multiple exit roads
  • the road segment includes multiple lanes
  • the basic road information includes the distance between the entry road and the lane.
  • the second connection relationship and the third connection relationship between the exit road and the lane, the determining the traffic network map according to the road basic information includes:
  • the lanes connected to each of the entrance roads are determined according to the second connection relationship, and the lanes connected to each of the exit roads are determined according to the third connection relationship said lane;
  • the traffic network graph is established, wherein the edges include the corresponding The driving direction of the lane.
  • a traffic network diagram is established with different lanes as edges, each lane has a corresponding driving direction, and a detection device is arranged on the edge. Since the edge includes the driving direction of the corresponding lane, the detected total flow of the edge can be distinguished. Traffic of vehicles with different steering directions. The deployment points of the detection equipment are located on the edge of the traffic network diagram.
  • any two of the nodes in the traffic network diagram form an OD pair, one of the nodes in the OD pair is the starting point, and the other node is the ending point, and it passes from the starting point to the ending point.
  • the path is the OD path corresponding to the OD pair, and the basic road information includes the prior flow of the OD pair and the prior flow of the OD path;
  • the optimization model of equipment placement points includes:
  • the prior flow of the OD path and the The sum of the traffic at the intersection is controlled by the signal, and the objective function of the optimization model is established by the first formula, and the first formula includes:
  • K 1 and K 2 are the weights of the objective function
  • w represents any of the OD pairs
  • W is the set of all the OD pairs in the traffic network diagram
  • r represents any corresponding OD pair w.
  • the OD path, R w is the set of all the paths of the OD to w
  • n represents any signal-controlled intersection
  • N S is the set of all the signal-controlled intersections in the traffic network diagram
  • y r is a 0-1 decision variable
  • q n is the sum of the traffic at the signal-controlled intersection n, if no For this data, q n can be set to 1, and s n is a 0-1 decision variable.
  • one part of the objective function represents the maximum sum of the traffic of the OD path that can be calculated from the data obtained by the detection device, and the other part represents the maximum sum of the traffic at the signal-controlled intersection detected by each detection device. Since the data detected by one detection device may include the traffic of multiple OD paths, it may not be possible to distinguish which OD path the data belongs to if only the data detected by one detection device is used. It is necessary to analyze the data detected by multiple detection devices. The OD path corresponding to the differentiated data is called uniquely identified by the detection device.
  • the solution is carried out to satisfy the constraints, and finally the specific value of the 0-1 decision variable is obtained, while the value of the 0-1 decision variable is obtained.
  • the specific value corresponds to the layout point of the detection equipment.
  • the detection device includes a cross-section flow detection device and a vehicle identity perception detection device, and the construction of the optimization model for the layout points of the multi-type detection devices according to the traffic network map and the basic road information further includes:
  • a second formula is used to determine the first constraint condition of the optimization model, and the second formula includes:
  • a any edge in the traffic network graph
  • r represents any of the OD paths
  • A is the set of all edges in the traffic network graph
  • the cross-section flow detection equipment can only detect the cross-section flow, including ground coils, geomagnetic and microwave radar, etc.
  • the vehicle identity awareness detection equipment can not only detect the cross-section flow, but also detect vehicle identity information, such as license plate numbers, etc., including electric police, bayonet and RFID etc.
  • the first constraint condition constrains that at least one edge of each OD path is installed with a detection device, so as to facilitate the detection of traffic with different turns at the signal-controlled intersection.
  • the optimization model for constructing the layout points of the multi-type detection equipment according to the traffic network map and the basic road information further includes:
  • a third formula is used to determine the second constraint condition of the optimization model, and the third formula includes:
  • r1 represents any of the OD paths that share a common edge with the path r.
  • any path r1 that has a common edge with path r if a detection device is arranged on the common edge, it is impossible to distinguish which path the detected vehicle belongs to, so at least one path can uniquely distinguish the path.
  • Detection equipment should be arranged on the edges of r and path r1, and path r should be distinguished from all paths r1.
  • the optimization model for constructing the layout points of the multi-type detection equipment according to the traffic network map and the basic road information further includes:
  • a fourth formula is used to determine the third constraint condition of the optimization model, and the fourth formula includes:
  • u a is the cost of installing the cross-section flow detection device on the side a
  • v a is the cost of installing the vehicle identity awareness detection device on the side a
  • B is the total budget.
  • the third constraint restricts the cost of installing testing equipment not to exceed the total budget. Under a certain budget constraint, using multiple types of testing equipment for integrated layout can achieve better testing results under the same budget. Coverage is wider.
  • the optimization model for constructing the layout points of the multi-type detection equipment according to the traffic network map and the basic road information further includes:
  • a fifth formula is used to determine the fourth constraint condition of the optimization model, and the fifth formula includes:
  • AS n is the set of all edges included in the signal control intersection n and connected to each entrance road, and a1 and a2 respectively represent two different edges.
  • a detection device is installed at each entrance of a signal-controlled intersection, it is considered that the signal-controlled intersection is covered by the detection device.
  • the solution of the optimization model to obtain the optimal combination of multiple types of detection equipment includes:
  • the optimization model is carried out to obtain the layout point of the detection device when the target value is the largest, wherein the target value is determined by the number of the OD paths that detect the flow of the detection device and the signal of the detected flow.
  • the number of control intersections is obtained by weighted summation.
  • the method for solving the optimization model is the prior art, which will not be repeated here.
  • an exhaustive method can be used to solve the optimization model, and each type of detection can be determined when the target value is the largest. The location of the equipment.
  • a device for laying out road detection equipment provided by an embodiment of the present invention includes:
  • the acquisition module is used to acquire the basic road information of the calibration area, and establish a traffic network diagram according to the basic road information, wherein the traffic network diagram includes OD pairs, OD paths, signal-controlled intersections, road sections and turning lanes, which are used to describe The signal controls the relationship between intersections, road sections, turning lanes and OD paths. Any two nodes in the traffic network diagram are one of the OD pairs, and the path between the OD pairs is the OD path, and the road
  • the basic information includes the total flow of the signal-controlled intersection, the prior flow of the OD pair and the prior flow of the OD path;
  • the building module is used to build an optimization model of the layout points of multi-type detection equipment according to the traffic network map and the basic information of the road, wherein, the objective function of the optimization model is based on the prior flow of the OD pair, all The prior flow of the OD path and the sum of the flow of the signal control intersection are established and obtained, the objective function of the optimization model is positively correlated with the number of the OD paths whose flow is detected by the detection device, and the objective function and The number of the signal-controlled intersections whose flow is detected by the detection device is positively correlated;
  • the constraints of the optimization model include that the detection device is installed on at least one road section on each of the OD paths, and for any of the OD paths path, at least one detection device is suitable for distinguishing the OD path from other OD paths, and the total cost of each type of the detection device is within the calibrated equipment budget;
  • the processing module is used to solve the optimization model to obtain the layout points of the detection equipment of various types.
  • Another embodiment of the present invention provides an apparatus for laying out road detection equipment, including a memory and a processor; the memory is used to store a computer program; the processor is used to implement the above-mentioned computer program when executing the computer program Road detection equipment layout method.
  • the device may be a computer or a processor or the like.
  • Another embodiment of the present invention provides a computer-readable storage medium with a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned method for laying out a road detection device is implemented.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM) or the like.
  • the unit described as a separate component may or may not be physically separated, and the component shown as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to many on a network unit.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

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Abstract

提供了一种道路检测设备布设方法、装置及存储介质，方法包括：获取标定区域的道路基本信息，根据道路基本信息建立交通网络图，其中，交通网络图用于描述路口、路段、转向车道以及OD路径之间的关系（S110）；根据交通网络图和道路基本信息构建多类型检测设备的布设点位的优化模型，其中，优化模型的目标函数与检测设备检测到流量的OD路径的数量正相关，且目标函数与检测设备检测到流量的信号控制路口的数量正相关（S120）；对优化模型进行求解，获得各个类型的检测设备的布设点位（S130）。由此布设各个类型的检测设备，能够提高检测的数据的全面性、减少设备投入。

Description

一种道路检测设备布设方法、装置及存储介质 技术领域
本发明涉及道路检测技术领域，具体而言，涉及一种道路检测设备布设方法、装置及存储介质。
背景技术
区域交通信号控制系统是指将城市道路网络的全部信号控制路口作为一个整体，联动起来进行交通信号优化控制的系统，相较于各个路口分别进行信号控制能够最大程度发挥信号控制系统的作用，保障交通安全和提高交通运行效率。区域交通信号控制采用多路径交通流协调、瓶颈路段的流入流出控制策略等实现区域整体的有效联动，不仅需要单个路口的流量，还需要整个道路网络的交通流OD(Origin-Destination，交通出行量)路径的需求流量等，为了获取这些信息就需要在道路网络上布设各种检测设备。
由于城市区域广、路网密度大等原因，若在每个路口和每个路段上都布设检测设备，所需的成本非常高。为了检测得到路网OD路径的需求流量，同时减少设备投入，目前常采用以下两种方法来布设检测设备。
一种是以交通OD矩阵估计为主要目标来布设断面流量检测设备，通常通过建立0-1线性规划模型(BILP，Binary Integer Linear Programming)，以检测设备的布设点位为决策变量，以能够覆盖的路网OD量最大为优化目标，在一定的设备数量或者预算的约束下，求解最优的断面流量检测设备的布设点位。但是，这种方法需要预先获取准确的先验OD信息，增加了工作量，并且依据该方法布设的检测设备仅适合检测交通流OD路径的需求流量。
另一种是以交通出行路径重构为主要目标来布设车辆身份感知检测设备，建立0-1线性规划模型，在全部OD路径被检测设备覆盖的情况下以设备数量最少为目标，或者在一定设备数量或预算的约束下，以检测设备覆盖的OD路径数最多为目标，求解车辆身份感知检测设备布设的点位，这种方法虽然不依赖准确的先验OD比例关系，但是依据该方法布设的检测设备仅适合检测交通流OD路径的需求流量。
发明内容
本发明解决的问题是针对现有技术中布设的检测设备采集的数据只包括OD路径流量，不够全面，且只能针对单一类型的检测设备布局进行优化的问题。
为解决上述问题，本发明提供一种道路检测设备布设方法、装置及存储介质。
第一方面，本发明提供了一种道路检测设备布设方法，包括：
获取标定区域的道路基本信息，根据所述道路基本信息建立交通网络图，其中，所述交通网络图包括OD对、OD路径、信号控制路口、路段和转向车道，用于描述信号控制路口、路段、转向车道和OD路径之间的关系，所述交通网络图中任意两个节点为一个所述OD对，所述OD对之间的路径为所述OD路径，所述道路基本信息包括所述信号控制路口的流量总和、所述OD对的先验流量和所述OD路径的先验流量；
根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型，其中，所述优化模型的目标函数由所述OD对的先验流量、所述OD路径的先验流量和所述信号控制路口的流量总和建立得到，所述优化模型的目标函数与所述检测设备检测到流量的所述OD路径的数量正相关，且所述目标函数与所述检测设备检测到流量的所述信号控制路口的数量正相关，所述优化模型的约束条件包括每条所述OD路径上至少有一个路段安装有所述检测设备，对于任一所述OD路径，至少有1个检测设备适于将所述OD路径与其它OD路径区分开，各个类型的所述检测设备的总成本在标定的设备预算内；
对所述优化模型进行求解，获得各个类型的所述检测设备的布设点位。
进一步，所述检测设备包括断面流量检测设备和车辆身份感知检测设备。
进一步，所述道路基本信息包括所述标定区域内的所述信号控制路口、路段和所述路段与所述信号控制路口之间的第一连接关系，所述根据所述道路基本信息建立交通网络图包括：
对于所述标定区域的任一所述信号控制路口，根据所述第一连接关系确定与所述信号控制路口连接的所有所述路段；
以所述信号控制路口为节点，以连接所述信号控制路口的各条所述路段为边建立所述交通网络图。
进一步，所述信号控制路口包括多个进口道和多个出口道，所述路段包括多个车道，所述道路基本信息包括所述进口道与所述车道之间的第二连接关系和所述出口道与所述车道之间的第三连接关系，所述根据所述道路基本信息建立交通网络图包括：
对于所述标定区域的任一所述信号控制路口，根据所述第二连接关系确定与各个所述进口道连接的所述车道，根据所述第三连接关系确定与各个所述出口道连接的所述车道；
以所述进口道和所述出口道为节点，以连接所述进口道和所述出口道的各条所述车道为边，建立所述交通网络图，其中，所述边包括对应的所述车道的行驶方向。
进一步，所述交通网络图中任意两个所述节点组成一个OD对，所述OD对中的一个所述节点为起点，另一个所述节点为终点，从所述起点到所述终点经过的路径为所述OD对对应的OD路径，所述道路基本信息包括OD对的先验流量和OD路径的先验流量；所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型包括：
以所述检测设备覆盖的所述OD路径的数量和所述信号控制路口的数量加权之和最大为优化目标，根据所述OD对的先验流量、所述OD路径的先验流量和所述信号控制路口的流量总和，采用第一公式建立所述优化模型的目标函数，所述第一公式包括：
Figure PCTCN2022070994-appb-000001
其中，K 1、K 2是所述目标函数的权重，w表示任一所述OD对，W是所述交通网络图中所有所述OD对的集合，r表示所述OD对w对应的任一OD路径，R w是所述OD对w的全部所述OD路径的集合，n表示任一所述信号控制路口，N S是所述交通网络图中全部所述信号控制路口的集合，
Figure PCTCN2022070994-appb-000002
表示所述OD对w的先验流量，
Figure PCTCN2022070994-appb-000003
表示所述路径r的先验流量，q n是所述 信号控制路口n的流量总和，y r和s n是0-1决策变量，当y r＝1时，OD路径r能被所述检测设备唯一识别，否则y r＝0，当s n＝1时，信号控制路口n的全部进口路段都安装有所述检测设备，否则s n＝0。
进一步，所述检测设备包括断面流量检测设备和车辆身份感知检测设备，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
采用第二公式确定所述优化模型的第一约束条件，所述第二公式包括：
Figure PCTCN2022070994-appb-000004
其中，a表示所述交通网络图中的任一条边，r表示任一条所述OD路径，A是所述交通网络图中全部边的集合；l a是0-1决策变量，当l a＝1时，表示在边a上安装有所述断面流量检测设备，当l a＝0时，则为所述边a上未安装所述断面流量检测设备；z a是0-1决策变量，当z a＝1时，表示在所述边a上安装有所述车辆身份感知检测设备，当z a＝0时，则表示所述边a上未安装所述车辆身份感知检测设备；
Figure PCTCN2022070994-appb-000005
是0-1变量，当
Figure PCTCN2022070994-appb-000006
时，表示所述OD路径r经过所述边a，否则
Figure PCTCN2022070994-appb-000007
进一步，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
采用第三公式确定所述优化模型的第二约束条件，所述第三公式包括：
Figure PCTCN2022070994-appb-000008
其中，r1表示与所述路径r有公用的边的任一所述OD路径。
进一步，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
采用第四公式确定所述优化模型的第三约束条件，所述第四公式包括：
∑ a∈A(u a×l a)+∑ a∈A(v a×z a)≤B，
其中，u a是在所述边a上安装所述断面流量检测设备的花费；v a是在所述边a上安装所述车辆身份感知检测设备的花费；B是总预算。
进一步，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
采用第五公式确定所述优化模型的第四约束条件，所述第五公式包括：
Figure PCTCN2022070994-appb-000009
其中，AS n是所述信号控制路口n包括的与各个进口道连接的所有边的集合，a1、a2分别表示两条不同的边。
进一步，所述对所述优化模型进行求解，获得各个类型的所述检测设备的布设点位包括：
对所述优化模型进行求解，获得目标值最大时所述检测设备的布设点位，其中，所述目标值由对所述检测设备检测到流量的所述OD路径数量和检测到流量的所述信号控制路口数量进行加权求和得到。
第二方面，本发明提供了一种道路检测设备布设装置，包括：
获取模块，用于获取标定区域的道路基本信息，根据所述道路基本信息建立交通网络图，其中，所述交通网络图包括OD对、OD路径、信号控制路口、路段和转向车道，用于描述信号控制路口、路段、转向车道和OD路径之间的关系，所述交通网络图中任意两个节点为一个所述OD对，所述OD对之间的路径为所述OD路径，所述道路基本信息包括所述信号控制路口的流量总和、所述OD对的先验流量和所述OD路径的先验流量；
构建模块，用于根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型，其中，所述优化模型的目标函数根据所述OD对的先验流量、所述OD路径的先验流量和所述信号控制路口的流量总和建立得到，所述优化模型的目标函数与所述检测设备检测到流量的所述OD路径的数量正相关，且所述目标函数与所述检测设备检测到流量的所述信号控制路口的数量正相关，所述优化模型的约束条件包括每条所述OD路径上至少有一个路段安装有所述检测设备，对于任一所述OD路径，至少有1个检测设备适于将所述OD路径与其它OD路径区分开，各个类型的所述检测设备的总成本在标定的设备预算内；
处理模块，用于对所述优化模型进行求解，获得各个类型的所述检测设备的布设点位。
第三方面，本发明提供了一种道路检测设备布设装置，包括存储器和处理器；
所述存储器，用于存储计算机程序；
所述处理器，用于当执行所述计算机程序时，实现如上所述的道路检测设备布设方法。
第四方面，本发明提供了一种计算机可读存储介质，所述存储介质上存储有计算机程序，当所述计算机程序被处理器执行时，实现如上所述的道路检测设备布设方法。
本发明的道路检测设备布设方法、装置及存储介质的有益效果是：获取标定区域的道路基本信息，道路基本信息可包括信号控制路口和路段等信息，根据道路基本信息建立交通网络图，交通网络图能够将复杂的道路交通网简单化，减少后续工作量，提高处理速度。交通网络图包括OD路径和信号控制路口，OD路径为从起点到终点经过的路径，根据交通网络构建多类型检测设备的布设点位的优化模型，结合多个不同类型的检测设备进行一体化布设，能够充分利用已有的检测设备，并且采用多类型检测设备相比于采用单一类型的检测设备，能够降低成本。在标定的设备预算约束下，优化模型的目标函数分别与检测设备检测到流量的OD路径数量和检测到流量的信号控制路口数量正相关，同时考虑对OD路径流量和信号控制路口流量的检测，对优化模型进行求解，得到各个类型检测设备的布设点位。根据该布设点位布设各个类型的检测设备，使得在标定的设备预算下，能够对更多的OD路径和信号控制路口的流量进行检测，在不增加设备成本的情况下，提高了检测数据的全面性。
附图说明
图1为本发明实施例的一种道路检测设备布设方法的流程示意图；
图2为本发明实施例的道路基本信息对应的道路交通网示意图；
图3为本发明实施例的信号控制路口示意图；
图4为本发明实施例的一种交通网络图；
图5为本发明实施例的另一种交通网络图；
图6为本发明实施例的一种道路检测设备布设装置的结构示意图。
具体实施方式
为使本发明的上述目的、特征和优点能够更为明显易懂，下面结合附图对本发明的具体实施例做详细的说明。
需要说明的是，本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本发明的实施例能够以除了在这里图示或描述的那些以外的顺序实施。
如图1所示，本发明实施例提供的一种道路检测设备布设方法，包括：
步骤110，获取标定区域的道路基本信息，根据所述道路基本信息建立交通网络图，其中，所述交通网络图包括OD对、OD路径、信号控制路口、路段和转向车道，用于描述信号控制路口、路段、转向车道和OD路径之间的关系，所述交通网络图中任意两个节点为一个所述OD对，所述OD对之间的路径为所述OD路径，所述道路基本信息包括所述信号控制路口的流量总和、所述OD对的先验流量和所述OD路径的先验流量；
步骤120，根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型，其中，所述优化模型的目标函数根据所述OD对的先验流量、所述OD路径的先验流量和所述信号控制路口的流量总和建立得到，所述优化模型的目标函数与所述检测设备检测到流量的所述OD路径的数量正相关，且所述目标函数与所述检测设备检测到流量的所述信号控制路口的数量正相关，所述优化模型的约束条件包括每条所述OD路径上至少有一个路段安装有所述检测设备，对于任一所述OD路径，至少有1个检测设备适于将所述OD路径与其它OD路径区分开，各个类型的所述检测设备的总成本在标定的设备预算内。
具体地，检测设备包括断面流量检测设备和车辆身份感知检测设备。优化模型以目标值最大为优化目标，目标值由对所述检测设备检测到流量的所 述OD路径数量和检测到流量的所述信号控制路口数量进行加权求和得到。
步骤130，对所述优化模型进行求解，获得各个类型的所述检测设备的布设点位。
本实施例中，获取标定区域的道路基本信息，道路基本信息可包括信号控制路口和路段等信息，根据道路基本信息建立交通网络图，交通网络图能够将复杂的道路交通网简单化，减少后续工作量，提高处理速度。交通网络图包括OD路径和信号控制路口，OD路径为从起点到终点经过的路径，根据交通网络构建多类型检测设备的布设点位的优化模型，结合多个不同类型的检测设备进行一体化布设，能够充分利用已有的检测设备，并且采用多类型检测设备相比于采用单一类型的检测设备，能够降低成本。在标定的设备预算约束下，优化模型的目标函数分别与检测设备检测到流量的OD路径数量和检测到流量的信号控制路口数量正相关，同时考虑对OD路径流量和信号控制路口流量的检测，对优化模型进行求解，得到各个类型检测设备的布设点位。根据该布设点位布设各个类型的检测设备，使得在标定的设备预算下，能够对更多的OD路径和信号控制路口的流量进行检测，在不增加设备成本的情况下，提高了检测数据的全面性。
具体地，如图2所示的道路交通网包括a-f共6个道路节点，和1-5共5条单向路段，6个道路节点组成了a→e、a→f、b→e、b→f共4个OD对，5条单向路段组成了1→3→4、1→3→5、2→3→4、2→3→5共4条路径，其中，每个OD对对应一条路径。
可在路段3上安装断面流量检测设备，在已知OD比例关系的先验信息下，就可根据断面流量检测设备检测的路段3的流量推算出各个OD对的流量。
或者在路段1、路段3和路段4上分别安装车辆身份感知检测设备，根据车辆身份感知检测设备检测到的车辆信息和流量信息可以直接推算出各个OD对的流量，而不需要预先知道各个OD对之间的比例关系。例如：在路段1和路段4都检测到的车辆就对应OD对a→e，在路段1没有被检测到而在路段4被检测到的车辆就对应OD对b→e，在路段1被检测到而在路段4未被检测到的车辆就对应OD对a→f，在路段3被检测到而路段1和路段4 未被检测到的车辆就对应OD对b→f。
采用本实施例的方案，可在路段1和路段4上分别安装身份感知检测设备，在路段3安装断面流量检测设备，也可以在不需要各OD对之间比例关系的情况下，推算出各条OD对的流量。例如：在路段1和路段4被检测到的车辆对应OD对a→e，在路段1没有被检测到而在路段4被检测到的车辆就对应OD对b→e，在路段1被检测到而路段4未被检测到的车辆就对应OD对a→f，用路段3检测到的总流量减去上述三个OD对的流量，就可得到OD对b→f的流量。
能够根据断面流量检测设备和车辆身份感知检测设备等多类型检测设备进行一体化布设，能够充分利用已有的检测设备，在实现相同的检测效果的情况下，相比于采用单一类型的检测设备，能够节省设备投入，降低成本。
优选地，如图3所示，所述道路基本信息包括所述标定区域内的所述信号控制路口、路段和所述路段与所述信号控制路口之间的第一连接关系，所述根据所述道路基本信息建立交通网络图包括：
对于所述标定区域的任一所述信号控制路口，根据所述第一连接关系确定与所述信号控制路口连接的所有所述路段；
以所述信号控制路口为节点，以连接所述信号控制路口的各条所述路段为边建立所述交通网络图。
具体地，如图4所示，将信号控制路口作为一个节点，若信号控制路口为十字路口，同一路段的不同行驶方向的车道为不同的边，则十字路口有八个与其连接的边，四条进入信号控制路口的边和四条离开信号控制路口的边。
优选地，如图5所示，所述信号控制路口包括多个进口道和多个出口道，所述路段包括多个车道，所述道路基本信息包括所述进口道与所述车道之间的第二连接关系和所述出口道与所述车道之间的第三连接关系，所述根据所述道路基本信息确定交通网络图包括：
对于所述标定区域的任一所述信号控制路口，根据所述第二连接关系确定与各个所述进口道连接的所述车道，根据所述第三连接关系确定与各个所述出口道连接的所述车道；
以所述进口道和所述出口道为节点，以连接所述进口道和所述出口道的 各条所述车道为边，建立所述交通网络图，其中，所述边包括对应的所述车道的行驶方向。
具体地，以不同的车道为边建立交通网络图，每个车道对应有行驶方向，在边上布设检测设备，由于边包括了对应车道的行驶方向，检测的该条边的总流量能够区分出不同转向的车的流量。检测设备的布设点位位于交通网络图的边上。
优选地，所述交通网络图中任意两个所述节点组成一个OD对，所述OD对中的一个所述节点为起点，另一个所述节点为终点，从所述起点到所述终点经过的路径为所述OD对对应的OD路径，所述道路基本信息包括OD对的先验流量和OD路径的先验流量；所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型包括：
以所述检测设备覆盖的所述OD路径的数量和所述信号控制路口的数量加权之和最大为优化目标，根据所述OD对的先验流量、所述OD路径的先验流量和所述信号控制路口的流量总和，采用第一公式建立所述优化模型的目标函数，所述第一公式包括：
Figure PCTCN2022070994-appb-000010
其中，K 1、K 2是所述目标函数的权重，w表示任一所述OD对，W是所述交通网络图中所有所述OD对的集合，r表示所述OD对w对应的任一所述OD路径，R w是所述OD对w的全部路径的集合，n表示任一信号控制路口，N S是所述交通网络图中全部所述信号控制路口的集合，
Figure PCTCN2022070994-appb-000011
表示所述OD对w的先验流量，
Figure PCTCN2022070994-appb-000012
表示所述OD路径r的先验流量，若没有先验流量的数据，可以将
Figure PCTCN2022070994-appb-000013
和
Figure PCTCN2022070994-appb-000014
设置为1，y r是0-1决策变量，当y r＝1，OD路径r能被检测设备唯一识别，否则y r＝0，q n是所述信号控制路口n的流量总和，若无该数据，可将q n设置为1，s n是0-1决策变量，当s n＝1，信号控制路口n的全部进口路段都安装有检测器，否则s n＝0。
具体地，目标函数中一部分表示通过检测设备获取的数据能够推算得到的OD路径的流量的总和最大，另一部分表示各个检测设备检测的信号控制 路口的流量总和最大。由于一个检测设备检测的数据可能包括多条OD路径的流量，若仅依靠一个检测设备检测的数据可能无法区分数据属于哪条OD路径，需要综合多个检测设备检测的数据进行分析，其中，能够被区分出来的数据对应的OD路径叫做能被检测设备唯一识别。目标函数中，通过调整K 1、K 2、q n、y r和s n的数值，进行求解使其满足约束条件，最终得到0-1决策变量的具体取值，而0-1决策变量的具体取值就对应了检测设备的布设点位。
优选地，所述检测设备包括断面流量检测设备和车辆身份感知检测设备，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
采用第二公式确定所述优化模型的第一约束条件，所述第二公式包括：
Figure PCTCN2022070994-appb-000015
其中，a表示所述交通网络图中的任一条边，r表示任一条所述OD路径，A是所述交通网络图中全部边的集合；l a是0-1决策变量，当l a＝1时，表示在所述边a上安装有所述断面流量检测设备，当l a＝0时，则为所述边a上未安装所述断面流量检测设备；z a是0-1决策变量，当z a＝1时，表示在所述边a上安装有所述车辆身份感知检测设备，当z a＝0时，则表示所述边a上未安装所述车辆身份感知检测设备；
Figure PCTCN2022070994-appb-000016
是0-1变量，当
Figure PCTCN2022070994-appb-000017
时，表示所述OD路径r经过所述边a，否则
Figure PCTCN2022070994-appb-000018
具体地，断面流量检测设备仅能检测断面流量，包括地面线圈、地磁和微波雷达等，车辆身份感知检测设备不仅能检测断面流量，还能检测车辆身份信息，例如车牌号等，包括电警、卡口和RFID等。第一约束条件约束每条OD路径上至少有一条边上安装有检测设备，便于检测通过信号控制路口的不同转向的流量。
优选地，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
采用第三公式确定所述优化模型的第二约束条件，所述第三公式包括：
Figure PCTCN2022070994-appb-000019
其中，r1表示与所述路径r有公用的边的任一所述OD路径。
具体地，对于路径r，任一与路径r有公用的边的路径r1，如果在公用边上布设检测设备，是无法区分检测到的车辆属于哪条路径的，因此至少有一个能够唯一区分路径r和路径r1的边上要布设有检测设备，且路径r要和所有路径r1区分开来。
优选地，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
采用第四公式确定所述优化模型的第三约束条件，所述第四公式包括：
∑ a∈A(u a×l a)+∑ a∈A(v a×z a)≤B，
其中，u a是在所述边a上安装所述断面流量检测设备的花费；v a是在所述边a上安装所述车辆身份感知检测设备的花费；B是总预算。
具体地，第三约束条件约束安装检测设备的花费不能超过总预算，在一定的预算约束下，采用多类型检测设备进行一体化布设，能够在同样的预算条件下，实现更好地检测效果，覆盖范围更广。
优选地，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
采用第五公式确定所述优化模型的第四约束条件，所述第五公式包括：
Figure PCTCN2022070994-appb-000020
其中，AS n是所述信号控制路口n包括的与各个进口道连接的所有边的集合，a1、a2分别表示两条不同的边。
具体地，信号控制路口的每个进口道都安装有检测设备，则认为该信号控制路口被检测设备所覆盖。
优选地，所述对所述优化模型进行求解，获得多类型检测设备的最优组合包括：
对所述优化模型进行，获得目标值最大时所述检测设备的布设点位，其中，所述目标值由对所述检测设备检测到流量的所述OD路径数量和检测到 流量的所述信号控制路口数量进行加权求和得到。
具体地，求解优化模型的方法为现有技术，在此不再赘述，例如：对于交通网络图中边较少的区域，可采用穷举法求解优化模型，确定目标值最大时各个类型的检测设备的布设点位。
如图6所示，本发明实施例提供的一种道路检测设备布设装置，包括：
获取模块，用于获取标定区域的道路基本信息，根据所述道路基本信息建立交通网络图，其中，所述交通网络图包括OD对、OD路径、信号控制路口、路段和转向车道，用于描述信号控制路口、路段、转向车道和OD路径之间的关系，所述交通网络图中任意两个节点为一个所述OD对，所述OD对之间的路径为所述OD路径，所述道路基本信息包括所述信号控制路口的流量总和、所述OD对的先验流量和所述OD路径的先验流量；
构建模块，用于根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型，其中，所述优化模型的目标函数根据所述OD对的先验流量、所述OD路径的先验流量和所述信号控制路口的流量总和建立得到，所述优化模型的目标函数与所述检测设备检测到流量的所述OD路径的数量正相关，且所述目标函数与所述检测设备检测到流量的所述信号控制路口的数量正相关；所述优化模型的约束条件包括每条所述OD路径上至少有一个路段安装有所述检测设备，对于任一所述OD路径，至少有1个检测设备适于将所述OD路径与其它OD路径区分开，各个类型的所述检测设备的总成本在标定的设备预算内；
处理模块，用于对所述优化模型进行求解，获得各个类型的所述检测设备的布设点位。
本发明另一实施例提供的一种道路检测设备布设装置包括存储器和处理器；所述存储器，用于存储计算机程序；所述处理器，用于当执行所述计算机程序时，实现如上所述的道路检测设备布设方法。该装置可为计算机或处理器等。
本发明再一实施例提供的一种计算机可读存储介质上存储有计算机程序，当所述计算机程序被处理器执行时，实现如上所述的道路检测设备布设方法。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)或随机存储记忆体(Random Access Memory，RAM)等。在本申请中，所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本发明实施例方案的目的。另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以是两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
虽然本公开披露如上，但本公开的保护范围并非仅限于此。本领域技术人员在不脱离本公开的精神和范围的前提下，可进行各种变更与修改，这些变更与修改均将落入本发明的保护范围。

Claims (13)

  1. 一种道路检测设备布设方法，其特征在于，包括：
    获取标定区域的道路基本信息，根据所述道路基本信息建立交通网络图，其中，所述交通网络图包括信号控制路口、OD对和OD路径，所述交通网络图中任意两个节点为一个所述OD对，所述OD对之间的路径为所述OD路径，所述道路基本信息包括所述信号控制路口的流量总和、所述OD对的先验流量和所述OD路径的先验流量；
    根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型，其中，所述优化模型的目标函数根据所述OD对的先验流量、所述OD路径的先验流量和所述信号控制路口的流量总和建立得到，所述优化模型的目标函数与所述检测设备检测到流量的所述OD路径的数量正相关，且所述目标函数与所述检测设备检测到流量的所述信号控制路口的数量正相关，所述优化模型的约束条件包括每条所述OD路径上至少有一个路段安装有所述检测设备，对于任一所述OD路径，至少有1个检测设备适于将所述OD路径与其它OD路径区分开，各个类型的所述检测设备的总成本在标定的设备预算内；
    对所述优化模型进行求解，获得各个类型的所述检测设备的布设点位。
  2. 根据权利要求1所述的道路检测设备布设方法，其特征在于，所述检测设备包括断面流量检测设备和车辆身份感知检测设备。
  3. 根据权利要求1所述的道路检测设备布设方法，其特征在于，所述道路基本信息包括所述标定区域内的所述信号控制路口、路段和所述路段与所述信号控制路口之间的第一连接关系，所述根据所述道路基本信息建立交通网络图包括：
    对于所述标定区域的任一所述信号控制路口，根据所述第一连接关系确定与所述信号控制路口连接的所有所述路段；
    以所述信号控制路口为节点，以连接所述信号控制路口的各条所述路段为边建立所述交通网络图。
  4. 根据权利要求3所述的道路检测设备布设方法，其特征在于，所述信号控制路口包括多个进口道和多个出口道，所述路段包括多个车道，所述道 路基本信息包括所述进口道与所述车道之间的第二连接关系和所述出口道与所述车道之间的第三连接关系，所述根据所述道路基本信息建立交通网络图包括：
    对于所述标定区域的任一所述信号控制路口，根据所述第二连接关系确定与各个所述进口道连接的所述车道，根据所述第三连接关系确定与各个所述出口道连接的所述车道；
    以所述进口道和所述出口道为节点，以连接所述进口道和所述出口道的各条所述车道为边，建立所述交通网络图，其中，所述边包括对应的所述车道的行驶方向。
  5. 根据权利要求3或4所述的道路检测设备布设方法，其特征在于，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型包括：
    以所述检测设备覆盖的所述OD路径的数量和所述信号控制路口的数量加权之和最大为优化目标，根据所述OD对的先验流量、所述OD路径的先验流量和所述信号控制路口的流量总和，采用第一公式建立所述优化模型的所述目标函数，所述第一公式包括：
    Figure PCTCN2022070994-appb-100001
    其中，K 1、K 2是所述目标函数的权重，w表示任一所述OD对，W是所述交通网络图中所有所述OD对的集合，r表示所述OD对w对应的任一OD路径，R w是所述OD对w的全部所述OD路径的集合，n表示任一所述信号控制路口，N S是所述交通网络图中全部所述信号控制路口的集合，
    Figure PCTCN2022070994-appb-100002
    表示所述OD对w的先验流量，
    Figure PCTCN2022070994-appb-100003
    表示所述路径r的先验流量，q n是所述信号控制路口n的流量总和，y r和s n是0-1决策变量，当y r＝1时，OD路径r能被所述检测设备唯一识别，否则y r＝0，当s n＝1时，信号控制路口n的全部进口路段都安装有所述检测设备，否则s n＝0。
  6. 根据权利要求5所述的道路检测设备布设方法，其特征在于，所述检测设备包括断面流量检测设备和车辆身份感知检测设备，所述根据所述交通 网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
    采用第二公式确定所述优化模型的第一约束条件，所述第二公式包括：
    Figure PCTCN2022070994-appb-100004
    其中，a表示所述交通网络图中的任一条边，r表示任一条所述OD路径，A是所述交通网络图中全部边的集合；l a是0-1决策变量，当l a＝1时，表示在边a上安装有所述断面流量检测设备，当l a＝0时，则为所述边a上未安装所述断面流量检测设备；z a是0-1决策变量，当z a＝1时，表示在所述边a上安装有所述车辆身份感知检测设备，当z a＝0时，则表示所述边a上未安装所述车辆身份感知检测设备；
    Figure PCTCN2022070994-appb-100005
    是0-1变量，当
    Figure PCTCN2022070994-appb-100006
    时，表示所述OD路径r经过所述边a，否则
    Figure PCTCN2022070994-appb-100007
  7. 根据权利要求6所述的道路检测设备布设方法，其特征在于，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
    采用第三公式确定所述优化模型的第二约束条件，所述第三公式包括：
    Figure PCTCN2022070994-appb-100008
    其中，r1表示与所述路径r有公用的边的任一所述OD路径。
  8. 根据权利要求7所述的道路检测设备布设方法，其特征在于，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
    采用第四公式确定所述优化模型的第三约束条件，所述第四公式包括：
    Figure PCTCN2022070994-appb-100009
    其中，u a是在所述边a上安装所述断面流量检测设备的花费；v a是在所述边a上安装所述车辆身份感知检测设备的花费；B是总预算。
  9. 根据权利要求8所述的道路检测设备布设方法，其特征在于，所述根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型还包括：
    采用第五公式确定所述优化模型的第四约束条件，所述第五公式包括：
    Figure PCTCN2022070994-appb-100010
    其中，AS n是所述信号控制路口n包括的与各个进口道连接的所有边的集合，a1、a2分别表示两条不同的边。
  10. 根据权利要求6至9任一项所述的道路检测设备布设方法，其特征在于，所述对所述优化模型进行求解，获得各个类型的所述检测设备的布设点位包括：
    对所述优化模型进行求解，获得目标值最大时所述检测设备的布设点位，其中，所述目标值由对所述检测设备检测到流量的所述OD路径数量和检测到流量的所述信号控制路口数量进行加权求和得到。
  11. 一种道路检测设备布设装置，其特征在于，包括：
    获取模块，用于获取标定区域的道路基本信息，根据所述道路基本信息建立交通网络图，其中，所述交通网络图包括OD对、OD路径和信号控制路口，所述交通网络图中任意两个节点为一个所述OD对，所述OD对之间的路径为所述OD路径，所述道路基本信息包括所述信号控制路口的流量总和、所述OD对的先验流量和所述OD路径的先验流量；
    构建模块，用于根据所述交通网络图和所述道路基本信息构建多类型检测设备的布设点位的优化模型，其中，所述优化模型的目标函数根据所述OD对的先验流量、所述OD路径的先验流量和所述信号控制路口的流量总和建立得到，所述优化模型的目标函数与所述检测设备检测到流量的所述OD路径的数量正相关，且所述目标函数与所述检测设备检测到流量的所述信号控制路口的数量正相关，所述优化模型的约束条件包括每条所述OD路径上至少有一个路段安装有所述检测设备，对于任一所述OD路径，至少有1个检测设备适于将所述OD路径与其它OD路径区分开，各个类型的所述检测设备的总成本在标定的设备预算内；
    处理模块，用于对所述优化模型进行求解，获得各个类型的所述检测设备的布设点位。
  12. 一种道路检测设备布设装置，其特征在于，包括存储器和处理器；
    所述存储器，用于存储计算机程序；
    所述处理器，用于当执行所述计算机程序时，实现如权利要求1至10任一项所述的道路检测设备布设方法。
  13. 一种计算机可读存储介质，其特征在于，所述存储介质上存储有计算机程序，当所述计算机程序被处理器执行时，实现如权利要求1至10任一项所述的道路检测设备布设方法。
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