CN115359663B - Mountain road disaster section disaster-resistant toughness calculation method and device and electronic equipment - Google Patents
Mountain road disaster section disaster-resistant toughness calculation method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and a device for calculating disaster-resistant toughness of a highway disaster section in a mountainous area and electronic equipment, and belongs to the technical field of highways. The method can quantitatively analyze the toughness of the road network, firstly, a core road section influencing the operation of the road network is determined from each road section of the road network as a target road section, the index toughness of the target road section is mainly considered, and the toughness value of the road network is indirectly determined through the index toughness of each target road section.
Description
Technical Field
The disclosure relates to the technical field of roads, in particular to a method and a device for calculating disaster-resistant toughness of a disaster-resistant road section of a mountain road.
Background
As the population rapidly grows and global warming increases the frequency and intensity of natural disasters, the urgency of tough traffic construction is increasingly prominent. In order to adapt to and deal with the influence of environmental factors such as climate change, natural disasters and the like, various countries actively build flexible traffic.
At present, geological disasters of roads in mountainous areas are frequent, and once important road sections are buried by landslides or broken by debris flows, traffic paralysis is even caused, and road rescue is difficult to perform. The road network system is complex, the evaluation indexes of the toughness of the road network are many, and the indexes are mutually influenced, so that the toughness of the road network cannot be evaluated.
Disclosure of Invention
This disclosure is provided to introduce concepts in a simplified form that are further described below in the detailed description. This disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The embodiment of the disclosure provides a method and a device for calculating disaster-resistant toughness of a highway disaster section in a mountain area, which can quantitatively analyze the toughness of a highway network, determine the toughness value of the highway network, facilitate reconstruction and upgrading of existing facilities, and enhance planning and management of expanding new roads.
In a first aspect, an embodiment of the present disclosure provides a method for calculating disaster-resistant toughness of a disaster-resistant road section in a mountain area, where the method includes:
determining each road junction of a target road network, forming a road section between each adjacent road junctions, weighting by taking the road junctions as points and the road sections as edges and taking section traffic flow to determine a road network model;
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value with huge node clusters, and determining a target road section influencing the smooth traffic of a traffic network;
determining the index toughness degree of each target road section based on the immunity index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
and determining a road network toughness value based on the index toughness of each target road section.
With reference to the embodiments of the first aspect, in some embodiments, the road network topology network model specifically includes:
Wherein, V represents a point set, E represents an edge set, and W represents a weight set.
With reference to the embodiments of the first aspect, in some embodiments, determining the road network service quality model based on the average vehicle speed of the road section and the traffic flow of the road section specifically includes:
determining a road network service quality model based on the average speed of vehicles on the road section and the traffic flow on the road section, wherein the road network service quality model is as follows:
In the formula,a service quality index set of the road network in a specified period d is obtained;serving as a road network service quality index in a road section mn at a time interval d;is the average speed of the vehicles for the road section mn,is the traffic flow of the road section mn in the time period d.
In combination with embodiments of the first aspect, in some embodiments,the running speed of the vehicle between the highway junction points m and n,the traffic flow between the highway junction points m and n in the time period d specifically comprises the following steps:
road sectionmnThe vehicle running speed of
In the formula,lmn is the distance of the highway road mn,t_mn is the traffic time of the highway section mn;
the traffic flow between the intersection points m and n of the highway in the time period d is
In the formula,Q (mn,d,1) the traffic flow of the vehicles on the highway section mn in the time period d;Q (mn,d,2) for road sections within time period dmn of the traffic flow of getting off;
Substituting the road section data of each road to obtain each road sectionTo correspond to the setThe network model is given the side weight.
With reference to the embodiments of the first aspect, in some embodiments, a threshold of the service quality of the road network is set and dynamically adjusted to a critical threshold where a huge node cluster appears, and a target road segment affecting smooth traffic of the traffic network is determined, specifically:
setting a road network service quality threshold value and obtaining the traffic state of each road section;
and dynamically adjusting the threshold value to a critical threshold value of a huge node cluster, enabling the whole network of the road network to be smooth, enabling the service quality of the road network to be the highest, and when the critical threshold value is slightly increased, enabling the whole network to be smooth and converted into local smooth, wherein the road section converted from the smooth road section into the crowded road section is the target road section.
With reference to the embodiments of the first aspect, in some embodiments, the index toughness of each target road segment is determined based on the noise immunity index, the adaptability index and the recovery index of each target road segment influencing the traffic network smoothness, including
Determining the immunity index of each target road section based on the scale of the potential disaster body, the influence range of the disaster and the probability of the potential disaster body, wherein the immunity index is calculated according to the formula:
In the formula,as an index of noise immunity for the target link mn,the scale characterization coefficient of the potential disaster body of the target road section mn is 0.2, wherein the scale of the disaster body is giant; the scale of the disaster body is large, and 0.4 is taken; the scale of the disaster body is small, and is 0.6; taking 1.0 without a disaster body;for the length of the disturbed road section in the road section mn,probability of occurrence of a potential disaster;
determining an adaptability index of each target road section based on the average speed of each target road section, wherein the calculation formula of the adaptability index is as follows:
based on the damage of each target road section, determining a restorability index according to the time for recovering the normal vehicle speed from the lower vehicle speed, wherein the restorability index is calculated according to the formula:
determining initial weights of an immunity index, an adaptability index and a recovery index based on an analytic hierarchy process;
determining influence indexes influencing the occurrence of traffic congestion states of all target road sections and the importance of the indexes based on the operation information of the target road network, acquiring the percentage of each index, and acquiring the experience weight of the index after normalization processing;
based on the initial weight and the empirical weight of the index, determining a dynamic weight according to an entropy weight method, wherein the dynamic weight is calculated according to a formula:
In the formula:is as followsIs a index ofThe dynamic weight after the second update;is as followsIs a index ofEntropy weights of the secondary updates;in order to update the number of times of the dynamic weight,is as followsAn index initial weight;is as followsThe empirical weight of each index;
based on the noise immunity index, the adaptability index, the recovery index and the corresponding dynamic weight, the toughness of each index, namely the noise immunity toughness, the adaptability toughness and the recovery toughness, is determined, wherein the toughness is the product of the index value and the corresponding dynamic weight.
With reference to the embodiments of the first aspect, in some embodiments, a road network toughness value is determined based on the index toughness of each target link, the noise immunity toughness, the adaptability toughness and the recovery toughness of each target link are subjected to a forward processing, a three-dimensional coordinate system with an origin O is established, the noise immunity index toughness, the adaptability index toughness and the recovery index toughness are respectively projected to the three-dimensional coordinate system, and an obtained vector mode length of each target link is the toughness value of the road network.
In a second aspect, an embodiment of the present disclosure provides that the apparatus includes:
constructing a model unit, wherein the model unit is used for determining each road junction of a target road network, a road section is formed between each two adjacent road junctions, the road junctions are taken as points, the road sections are taken as edges, and the cross section traffic flow is weighted to determine a road network model;
the method comprises the steps of determining a target unit, wherein the target unit is used for determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value with huge node clusters, and determining a target road section influencing the smooth traffic of a traffic network;
the solving unit is used for determining the index toughness of each target road section based on the immunity index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
a determination unit for determining a road network toughness value based on the index toughness of each target road segment.
With reference to the embodiments of the second aspect, in some embodiments, the determining the road network service quality model based on the average vehicle speed of the road section and the traffic flow of the road section is specifically:
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, wherein the road network service quality model is as follows:
In the formula,a service quality index set of the road network in a specified period d is obtained;serving as a road network service quality index in a road section mn at a time interval d;is the average speed of the vehicles for the road section mn,is the traffic flow of the road section mn in the time period d.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device, configured to store one or more programs, where when the one or more programs are executed by the one or more processors, the one or more processors implement the method for calculating disaster-resistant toughness for the disaster-resistant road section on the mountain road according to the first aspect.
The method for calculating disaster-resistant toughness of the highway disaster section in the mountainous area provided by the embodiment of the disclosure can quantitatively analyze the toughness of the highway network, firstly, a core road section influencing the operation of the highway network is determined from each road section of the highway network as a target road section, the index toughness of the target road section is mainly considered, and the toughness value of the highway network is indirectly determined through the index toughness of each target road section.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1 is a flowchart of one embodiment of a method for calculating disaster resistant toughness for a mountain highway disaster section according to the present disclosure;
fig. 2 is a schematic diagram of a basic structure of an electronic device provided according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Referring to fig. 1, a flow of an embodiment of a disaster-resistant toughness calculation method for a disaster-resistant road section in a mountain road according to the disclosure is shown. The method for calculating disaster-resistant toughness of the mountain road disaster section can be applied to the evaluation of the toughness of the road network, but is not limited thereto. As shown in fig. 1, the method for calculating disaster-resistant toughness of the mountain road disaster section includes the following steps:
Here, the road network topology network model specifically includes:
Wherein, V represents a point set, E represents an edge set, and W represents a weight set.
Here, it sets the road network service quality threshold value by means of the seepage theory, and when dynamically adjusting the threshold value, if the critical threshold value of huge node cluster appears, the congested road section.
The road network service quality model is determined based on the average speed of vehicles on the road section and the traffic flow on the road section and specifically comprises the following steps:
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, wherein the road network service quality model is as follows:
In the formula,a service quality index set of the road network in a specified period d is obtained;service quality indexes of the road network in the road section mn under the time interval d;is the average speed of the vehicles for the road section mn,is the traffic flow of the road section mn in the time period d.
Wherein,the running speed of the vehicle between the highway junction points m and n,the traffic flow between the highway junction points m and n in the time period d specifically comprises the following steps:
road sectionmnThe vehicle running speed of
In the formula,lmn is the distance of the highway road mn,t_mn is the traffic time of the highway section mn;
the traffic flow between the intersection points m and n of the highway in the time period d is
In the formula,Q (mn,d,1) the traffic flow of the vehicle on the road section mn in the time period d is shown;Q (mn,d,2) the traffic flow of getting off the highway section mn in the time period d is obtained;
Substituting the road section data of each road to obtain each road sectionTo correspond to the setThe network model is given the edge weight.
Further, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value of a huge node cluster, and determining a target road section influencing the smooth traffic of a traffic network, wherein the specific steps are as follows:
setting a road network service quality threshold value and obtaining the traffic state of each road section;
and dynamically adjusting the threshold value to the critical threshold value of the huge node cluster, enabling the whole network of the road network to be smooth, enabling the service quality of the road network to be the highest, when the critical threshold value is slightly increased, enabling the whole network to be smooth and converted into local smooth, and enabling the road section converted from the smooth section into the crowded section to be the target section.
And 103, determining the index toughness of each target road section based on the interference resistance index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network.
The method comprises the steps of determining the immunity index of each target road section based on the scale of the potential disaster body, the influence range of the disaster and the probability of the occurrence of the potential disaster body; determining an adaptability index of each target road section based on the average speed of each target road section; determining a restorability index according to the time for recovering the normal speed from the lower speed based on the damage of each target road section; determining initial weights of an immunity index, an adaptability index and a recovery index based on an analytic hierarchy process; determining influence indexes and index importance influencing the occurrence of traffic congestion states of all target road sections based on operation information of a target road network, acquiring percentage of each index, and acquiring experience weights of the indexes after normalization processing; determining a dynamic weight according to an entropy weight method based on the initial weight and the empirical weight of the index; based on the noise immunity index, the adaptability index, the recovery index and the corresponding dynamic weight, the toughness degrees of the indexes, namely the noise immunity toughness degree, the adaptability toughness degree and the recovery toughness degree, are determined.
Illustratively, the immunity indicator calculation formula:
In the formula,as an index of noise immunity for the target link mn,the scale representation coefficient of the potential disaster body of the target road section mn is 0.2, wherein the scale of the disaster body is giant; the scale of the disaster body is large, and 0.4 is taken; the scale of the disaster body is small, and is 0.6; taking 1.0 without a disaster body;for the length of the disturbed road section in the road section mn,probability of occurrence of a potential disaster;
the adaptability index calculation formula is as follows:
The recovery index calculation formula is as follows:
Dynamic weight calculation formula:
In the formula:is a firstIs a index ofThe dynamic weight after the second update;is as followsIs a index ofEntropy weights of the secondary updates;in order to update the number of times of the dynamic weight,is as followsEach index initial weight;is a firstIndividual index empirical weights.
The toughness is the product of the index value and the corresponding dynamic weight.
And 104, determining a road network toughness value based on the index toughness of each target road section.
Here, the target link toughness degree, the adaptive toughness degree, and the restoration toughness degree are normalized to create a three-dimensional coordinate system with the origin O as the origin, and the noise immunity index toughness degree, the adaptive index toughness degree, and the restoration index toughness degree are projected to the three-dimensional coordinate system, and the obtained vector mode length of the target link is the toughness value of the road network.
The method for calculating disaster-resistant toughness of the road disaster section in the mountainous area can quantitatively analyze the toughness of the road network, firstly, a core road section influencing the operation of the road network is determined from each road section of the road network as a target road section, the index toughness of the target road section is considered, and the toughness value of the road network is indirectly determined through the index toughness of each target road section.
Further, as an implementation of the method shown in the above figures, the present disclosure provides a device for calculating disaster-resistant toughness of a disaster-resistant road section in a mountain area, where an embodiment of the device corresponds to the embodiment of the method shown in fig. 1, and the device may be applied to various electronic devices.
The mountain road disaster section disaster-resistant toughness calculation device of the embodiment includes:
constructing a model unit, wherein the model unit is used for determining each road junction of a target road network, a road section is formed between each two adjacent road junctions, the road junction is taken as a point, the road section is taken as an edge, and the weighting is carried out by taking the traffic flow of a cross section to determine a road network model;
the system comprises a target determining unit, a traffic monitoring unit and a traffic monitoring unit, wherein the target determining unit is used for determining a road network service quality model based on the average speed of vehicles on a road section and the traffic flow of the road section, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value with huge node clusters, and determining a target road section influencing the smooth traffic of a traffic network;
the solving unit is used for determining the index toughness of each target road section based on the immunity index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
a determination unit for determining a road network toughness value based on the index toughness of each target road segment.
Referring now to FIG. 2, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 2, the electronic device may include a processing apparatus (e.g., a central processing unit, a graphics processor, etc.) 201 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 202 or a program loaded from a storage apparatus 208 into a Random Access Memory (RAM) 203. In the RAM203, various programs and data necessary for the operation of the electronic apparatus 200 are also stored. The processing device 201, the ROM202, and the RAM203 are connected to each other via a bus 204. An input/output (M/O) interface 205 is also connected to bus 204.
Generally, the following devices may be connected to the M/O interface 205: input devices 206 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 207 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 208 including, for example, magnetic tape, hard disk, etc.; and a communication device 202. The communication means 202 may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While fig. 2 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 209, or installed from the storage means 208, or installed from the ROM 202. The computer program, when executed by the processing device 201, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Nava, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the names of the modules do not in some cases constitute a limitation of the unit itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application specific integrated circuits (ASMCs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other combinations of features described above or equivalents thereof without departing from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (6)
1. A method for calculating disaster-resistant toughness of a disaster-resistant road section of a mountain road is characterized by comprising the following steps:
determining each road junction of a target road network, forming a road section between each adjacent road junctions, weighting by taking the road junctions as points and the road sections as edges and taking section traffic flow to determine a road network model;
determining a road network service quality model based on the average speed of vehicles on the road section and the traffic flow on the road section, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value of a huge node cluster, and determining a target road section influencing the smooth traffic of a traffic network;
determining the index toughness of each target road section based on the interference resistance index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
determining a road network toughness value based on the index toughness of each target road section;
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, wherein the road network service quality model is as follows:
In the formula,the method comprises the steps of collecting service quality indexes of a road network in a specified time period d;serving as a road network service quality index in a road section mn at a time interval d;is the average speed of the vehicles for the road section mn,the traffic flow of the road section mn in the time period d;
determining the toughness of each target road section based on the interference resistance index, adaptability index and recovery index of each target road section influencing the smoothness of the traffic network, comprising
Determining the immunity index of each target road section based on the scale of the potential disaster body, the influence range of the disaster and the probability of the potential disaster body, wherein the immunity index is calculated according to the formula:
In the formula,for the noise immunity indicator of the target road section mn,the scale characterization coefficient of the potential disaster body of the target road section mn is 0.2, wherein the scale of the disaster body is giant; the scale of the disaster body is large, and 0.4 is taken; the scale of the disaster body is small, and 0.6 is taken; taking 1.0 without a disaster body;for the length of the disturbed road section in the road section mn,probability of occurrence of a potential disaster;
determining an adaptability index of each target road section based on the average speed of each target road section, wherein the calculation formula of the adaptability index is as follows:
determining a restorability index according to the time for recovering the normal speed from the lower speed based on the damage of each target road section, wherein the restorability index is calculated according to the formula:
determining an initial weight of an immunity index, an adaptability index and a restorability index based on an analytic hierarchy process;
determining influence indexes influencing the occurrence of traffic congestion states of all target road sections and the importance of the indexes based on the operation information of the target road network, acquiring the percentage of each index, and acquiring the experience weight of the index after normalization processing;
based on the initial weight and the empirical weight of the index, determining a dynamic weight according to an entropy weight method, wherein the dynamic weight is calculated according to a formula:
In the formula:is as followsIs a index ofThe dynamic weight after the second update;is as followsIs a index ofEntropy weights of the secondary updates;in order to update the number of times of the dynamic weight,is a firstEach index initial weight;is as followsThe empirical weight of each index; determining toughness degrees of all indexes, namely, the immunity toughness degree, the adaptability toughness degree and the restoration toughness degree, based on the immunity index, the adaptability index, the restoration index and the corresponding dynamic weight, wherein the toughness degree is the product of the index value and the corresponding dynamic weight;
determining a road network toughness value based on the index toughness of each target road segment, comprising
And performing forward processing on the immunity toughness, the adaptability toughness and the recovery toughness of each target road section, establishing a three-dimensional coordinate system with O as an origin, respectively projecting the immunity index toughness, the adaptability index toughness and the recovery index toughness to the three-dimensional coordinate system, and obtaining the vector mode length of the target road section, namely the toughness value of the road network.
3. The method for calculating disaster-resistant toughness of a disaster-resistant road section in a mountain area according to claim 2,the running speed of the vehicle between the highway junction points m and n,the traffic flow between the highway junction points m and n in the time period d specifically comprises the following steps:
road sectionmnThe vehicle running speed of
In the formula,lmn is the distance of the road section mn,tmn is the passing time of the road section mn;
the traffic flow between the intersection points m and n of the highway in the time period d is
In the formula,Q (mn,d,1) the traffic flow of the vehicle on the road section mn in the time period d is shown;Q (mn,d,2) the traffic flow of getting off the highway section mn in the time period d is obtained;
4. The method for calculating the disaster-resistant toughness of the mountain road disaster section according to claim 3, wherein the method comprises the steps of setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value at which a huge node cluster appears, and determining a target section affecting the smooth traffic network, specifically:
setting a road network service quality threshold value and obtaining the traffic state of each road section;
and dynamically adjusting the threshold value to the critical threshold value of the huge node cluster, enabling the whole network of the road network to be smooth, enabling the service quality of the road network to be the highest, when the critical threshold value is slightly increased, enabling the whole network to be smooth and converted into local smooth, and enabling the road section converted from the smooth section into the crowded section to be the target section.
5. Mountain area highway disaster resistant highway section disaster resistant toughness computing device, its characterized in that, the device includes:
constructing a model unit, wherein the model unit is used for determining each road junction of a target road network, a road section is formed between each two adjacent road junctions, the road junctions are taken as points, the road sections are taken as edges, and the cross section traffic flow is weighted to determine a road network model;
the system comprises a target determining unit, a traffic monitoring unit and a traffic monitoring unit, wherein the target determining unit is used for determining a road network service quality model based on the average speed of vehicles on a road section and the traffic flow of the road section, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value at which huge node clusters appear, and determining a target road section influencing the smooth traffic of a traffic network;
the solving unit is used for determining the index toughness of each target road section based on the immunity index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
a determination unit for determining a road network toughness value based on the index toughness of each target road segment;
the road network service quality model is determined based on the average speed of vehicles on the road sections and the traffic flow of the road sections, wherein the road network service quality model is as follows:
In the formula,a service quality index set of the road network in a specified period d is obtained;service quality indexes of the road network in the road section mn under the time interval d;is the average speed of the vehicles for the road section mn,the traffic flow of the road section mn in the time period d;
determining the toughness of each target road section based on the interference resistance index, adaptability index and recovery index of each target road section influencing the smoothness of the traffic network, comprising
Determining the immunity index of each target road section based on the scale of the potential disaster body, the influence range of the disaster and the probability of the potential disaster body, wherein the immunity index is calculated by the formula:
In the formula,as an index of noise immunity for the target link mn,the scale characterization coefficient of the potential disaster body of the target road section mn is 0.2, wherein the scale of the disaster body is giant; the scale of the disaster body is large, and 0.4 is taken; the scale of the disaster body is small, and 0.6 is taken; taking 1.0 without a disaster body;for the length of the disturbed road section in the road section mn,probability of occurrence of a potential disaster;
determining an adaptability index of each target road section based on the average speed of each target road section, wherein the calculation formula of the adaptability index is as follows:
determining a restorability index according to the time for recovering the normal speed from the lower speed based on the damage of each target road section, wherein the restorability index is calculated according to the formula:
determining an initial weight of an immunity index, an adaptability index and a restorability index based on an analytic hierarchy process;
determining influence indexes influencing the occurrence of traffic congestion states of all target road sections and the importance of the indexes based on the operation information of the target road network, acquiring the percentage of each index, and acquiring the experience weight of the index after normalization processing;
based on the initial weight and the empirical weight of the index, determining a dynamic weight according to an entropy weight method, wherein the dynamic weight is calculated according to a formula:
In the formula:is as followsIs a first indexThe dynamic weight after the second update;is as followsIs a index ofEntropy weights of the secondary updates;in order to update the number of times of the dynamic weight,is a firstEach index initial weight;is as followsThe empirical weight of each index; determining toughness degrees of all indexes, namely, the immunity toughness degree, the adaptability toughness degree and the restoration toughness degree, based on the immunity indexes, the adaptability indexes, the restoration indexes and the corresponding dynamic weights, wherein the toughness degree is the product of the index value and the corresponding dynamic weight;
determining a road network toughness value based on the index toughness of each target road segment, comprising
And carrying out forward processing on the immunity toughness, the adaptability toughness and the recovery toughness of each target road section, establishing a three-dimensional coordinate system with O as an origin, respectively projecting the immunity index toughness, the adaptability index toughness and the recovery index toughness to the three-dimensional coordinate system, and obtaining the vector modular length of the target road section, namely the toughness value of the road network.
6. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method for disaster-resistant toughness calculation for a mountain highway disaster section as recited in any of claims 1-4.
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Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6312188B1 (en) * | 1996-06-27 | 2001-11-06 | General Dynamics Ordnance And Tactical Systems, Inc. | Non-lethal, rapidly deployed vehicle immobilizer |
CN104269051A (en) * | 2014-10-17 | 2015-01-07 | 成都四为电子信息股份有限公司 | Expressway monitoring and management system |
CN104361746A (en) * | 2014-11-05 | 2015-02-18 | 上海新炬网络信息技术有限公司 | Intelligent highway traffic monitoring method |
CN104658253A (en) * | 2015-02-14 | 2015-05-27 | 浙江大学 | Highway traffic state identification method |
CN105303831A (en) * | 2015-10-20 | 2016-02-03 | 四川公用信息产业有限责任公司 | Method for determining congestion state of highway based on communication data |
CN108876203A (en) * | 2018-07-26 | 2018-11-23 | 中国地震局工程力学研究所 | Function toughness evaluation method and apparatus after road traffic shake |
CN111080136A (en) * | 2019-12-19 | 2020-04-28 | 四川省公路规划勘察设计研究院有限公司 | Risk quantitative evaluation method and device for geological disaster chain after strong earthquake |
JP2020112863A (en) * | 2019-01-08 | 2020-07-27 | 富士通株式会社 | Site evaluation program, site evaluation method and site evaluation device |
CN113667292A (en) * | 2021-08-24 | 2021-11-19 | 桂迎生 | Polymer elastic composite material and highway bridge telescoping device |
CN114014210A (en) * | 2022-01-05 | 2022-02-08 | 四川省公路规划勘察设计研究院有限公司 | Road rescue in-situ transferring device for improving toughness of road network |
CN114093168A (en) * | 2021-11-18 | 2022-02-25 | 长安大学 | Urban road traffic running state evaluation method based on toughness view angle |
CN114427204A (en) * | 2022-01-07 | 2022-05-03 | 四川省公路规划勘察设计研究院有限公司 | Key road section road recovery rescue equipment considering landslide hazard |
CN114446051A (en) * | 2022-01-06 | 2022-05-06 | 东南大学 | Method for identifying weak toughness of urban road network traffic |
CN114639241A (en) * | 2022-03-15 | 2022-06-17 | 北京交通大学 | Method and system for judging road section interruption state |
EP4030376A1 (en) * | 2021-01-16 | 2022-07-20 | Bayerische Motoren Werke Aktiengesellschaft | Method for determining an impact of ride pooling on a traffic situation |
CN115094697A (en) * | 2022-07-01 | 2022-09-23 | 中铁二十局集团第二工程有限公司 | Highway sealing layer construction method |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20070294023A1 (en) * | 2006-06-19 | 2007-12-20 | Navteq North America, Llc | Traffic data collection with probe vehicles |
US7813843B2 (en) * | 2007-01-04 | 2010-10-12 | Cisco Technology, Inc | Ad-hoc mobile IP network for intelligent transportation system |
US10922965B2 (en) * | 2018-03-07 | 2021-02-16 | Here Global B.V. | Method, apparatus, and system for detecting a merge lane traffic jam |
WO2021138010A1 (en) * | 2019-12-31 | 2021-07-08 | Nvidia Corporation | Three-dimensional intersection structure prediction for autonomous driving applications |
CN111595672A (en) * | 2020-05-08 | 2020-08-28 | 北京市高强混凝土有限责任公司 | Anti-disturbance evaluation method for indoor anti-disturbance concrete |
CN112508392B (en) * | 2020-12-02 | 2021-11-02 | 云南省交通规划设计研究院有限公司 | Dynamic evaluation method for traffic conflict risk of hidden danger road section of mountain area double-lane highway |
-
2022
- 2022-10-21 CN CN202211290040.5A patent/CN115359663B/en active Active
Patent Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6312188B1 (en) * | 1996-06-27 | 2001-11-06 | General Dynamics Ordnance And Tactical Systems, Inc. | Non-lethal, rapidly deployed vehicle immobilizer |
CN104269051A (en) * | 2014-10-17 | 2015-01-07 | 成都四为电子信息股份有限公司 | Expressway monitoring and management system |
CN104361746A (en) * | 2014-11-05 | 2015-02-18 | 上海新炬网络信息技术有限公司 | Intelligent highway traffic monitoring method |
CN104658253A (en) * | 2015-02-14 | 2015-05-27 | 浙江大学 | Highway traffic state identification method |
CN105303831A (en) * | 2015-10-20 | 2016-02-03 | 四川公用信息产业有限责任公司 | Method for determining congestion state of highway based on communication data |
CN108876203A (en) * | 2018-07-26 | 2018-11-23 | 中国地震局工程力学研究所 | Function toughness evaluation method and apparatus after road traffic shake |
JP2020112863A (en) * | 2019-01-08 | 2020-07-27 | 富士通株式会社 | Site evaluation program, site evaluation method and site evaluation device |
CN111080136A (en) * | 2019-12-19 | 2020-04-28 | 四川省公路规划勘察设计研究院有限公司 | Risk quantitative evaluation method and device for geological disaster chain after strong earthquake |
EP4030376A1 (en) * | 2021-01-16 | 2022-07-20 | Bayerische Motoren Werke Aktiengesellschaft | Method for determining an impact of ride pooling on a traffic situation |
CN113667292A (en) * | 2021-08-24 | 2021-11-19 | 桂迎生 | Polymer elastic composite material and highway bridge telescoping device |
CN114093168A (en) * | 2021-11-18 | 2022-02-25 | 长安大学 | Urban road traffic running state evaluation method based on toughness view angle |
CN114014210A (en) * | 2022-01-05 | 2022-02-08 | 四川省公路规划勘察设计研究院有限公司 | Road rescue in-situ transferring device for improving toughness of road network |
CN114446051A (en) * | 2022-01-06 | 2022-05-06 | 东南大学 | Method for identifying weak toughness of urban road network traffic |
CN114427204A (en) * | 2022-01-07 | 2022-05-03 | 四川省公路规划勘察设计研究院有限公司 | Key road section road recovery rescue equipment considering landslide hazard |
CN114639241A (en) * | 2022-03-15 | 2022-06-17 | 北京交通大学 | Method and system for judging road section interruption state |
CN115094697A (en) * | 2022-07-01 | 2022-09-23 | 中铁二十局集团第二工程有限公司 | Highway sealing layer construction method |
Non-Patent Citations (3)
Title |
---|
基于韧性最优的灾后公路网修复调度研究;毛新华 等;《中国公路学报》;20220630;第35卷(第6期);第289-298页 * |
映汶高速公路桥梁防震减灾技术的应用;殷强 等;《公路交通技术》;20140805;第4卷;第57-60+71页 * |
韧性城市研究新进展;李彤;《国际城市规划》;20171231(第05期);第19-29页 * |
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