Traffic network health portrait method and system based on fuzzy clustering
1. A traffic network health image method based on fuzzy clustering is characterized by comprising the following steps:
obtaining vehicle road information in set time in a road network region to be analyzed; the vehicle road information includes: the road number, the road starting point number, the road end point number, the road grade, the road information, the road vehicle speed and the longitude and latitude information at each moment;
preprocessing the vehicle road information, and constructing an urban traffic flow network of the road network region to be analyzed according to the preprocessed data;
determining the health state of the urban traffic flow network at different moments by adopting a seepage analysis method;
dividing the health states of the urban traffic flow network at different moments by adopting a clustering analysis method;
and performing health portrayal on the urban traffic flow network of the road network area to be analyzed by using the division result.
2. The traffic network health imaging method based on fuzzy clustering according to claim 1, wherein the preprocessing is performed on the vehicle road information, and the urban traffic flow network of the road network region to be analyzed is constructed according to the preprocessed data, specifically comprising:
matching the road information with the speed of the road vehicle at each moment; completing the speed of the missing road vehicle;
matching the road starting point number, the road end point number and the longitude and latitude;
and constructing the urban traffic flow network by taking the roads in the road network area to be analyzed as the connecting edges of the urban traffic flow network, taking the intersections as the nodes of the urban traffic flow network and taking the real-time relative vehicle speed of each road as the weight of the connecting edges.
3. The traffic network health imaging method based on fuzzy clustering according to claim 1, wherein the determining the health status of the urban traffic flow network at different times by using a seepage analysis method specifically comprises:
acquiring a relative speed threshold; the relative speed threshold is a numerical value which is changed from 0 to 1 according to set precision;
deleting the connecting edges of each road in the urban traffic flow network, the real-time relative vehicle speed of which is less than the relative speed threshold value at the corresponding moment, and determining the maximum connected sub-cluster size of the urban traffic flow network at the corresponding moment;
determining G-q curve graphs at different moments according to the maximum connected sub-cluster size of the urban traffic flow network at each moment and the corresponding relative speed threshold; wherein G is the size of the maximum connected cluster, and q is a relative speed threshold;
converting G-q curve graphs at different moments into G-f curve graphs; wherein f is the ratio of the deleted connecting edge to the total number of edges of the urban traffic flow network;
and determining the health state of the urban traffic flow network at different moments according to the G-f curve graphs at different moments.
4. The traffic network health imaging method based on fuzzy clustering according to claim 3, wherein the method of cluster analysis is used to classify the health status of the urban traffic flow network at different times, which specifically comprises:
determining clustering characteristic vectors at corresponding moments according to G-f curve graphs at different moments;
and carrying out fuzzy clustering on the feature vectors by utilizing an FCM clustering algorithm to classify the health state of the urban traffic flow network into healthy or unhealthy.
5. The traffic network health image method based on fuzzy clustering as claimed in claim 1, wherein said performing health image on urban traffic flow network of road network region to be analyzed by using division result specifically comprises:
respectively determining network structure indexes and network operation indexes of healthy urban traffic flow networks and unhealthy urban traffic flow networks; the network structure indicators include: average degree and dynamic betweenness; the network operation indicators include: TTI index and network average speed;
the network structure index and the network operation index respectively carry out health portrayal on the corresponding urban traffic flow network.
6. A traffic network health representation system based on fuzzy clustering is characterized by comprising:
the vehicle road information acquisition module is used for acquiring vehicle road information in set time in a road network region to be analyzed; the vehicle road information includes: the road number, the road starting point number, the road end point number, the road grade, the road information, the road vehicle speed and the longitude and latitude information at each moment;
the urban traffic flow network construction module is used for preprocessing the vehicle road information and constructing an urban traffic flow network of the road network area to be analyzed according to the preprocessed data;
the health state determining module of the urban traffic flow network is used for determining the health states of the urban traffic flow network at different moments by adopting a seepage analysis method;
the division result determining module is used for dividing the health states of the urban traffic flow network at different moments by adopting a clustering analysis method;
and the health portrait module is used for carrying out health portrait on the urban traffic flow network of the road network area to be analyzed by utilizing the division result.
7. The traffic network health representation system based on fuzzy clustering according to claim 6, wherein the urban traffic flow network construction module specifically comprises:
a first matching unit for matching the road information with the road vehicle speed at each moment; completing the speed of the missing road vehicle;
the second matching unit is used for matching the road starting point number, the road end point number and the longitude and latitude;
and the urban traffic flow network construction unit is used for constructing the urban traffic flow network by taking the roads in the road network area to be analyzed as the connecting edges of the urban traffic flow network, taking the intersections as the nodes of the urban traffic flow network and taking the real-time relative vehicle speed of each road as the connecting edge weight.
8. The system for representing a health of a traffic network based on fuzzy clustering as claimed in claim 6, wherein the module for determining the health status of the urban traffic flow network comprises:
a relative speed threshold value acquisition unit for acquiring a relative speed threshold value; the relative speed threshold is a numerical value which is changed from 0 to 1 according to set precision;
the maximum connected sub-cluster size determining unit is used for deleting the connected edges of each road in the urban traffic flow network, of which the real-time relative vehicle speed is smaller than the relative speed threshold at the corresponding moment, and determining the maximum connected sub-cluster size of the urban traffic flow network at the corresponding moment;
the G-q curve graph determining unit is used for determining G-q curve graphs at different moments according to the maximum connected sub-cluster size of the urban traffic flow network at each moment and the corresponding relative speed threshold; wherein G is the size of the maximum connected cluster, and q is a relative speed threshold;
the conversion unit is used for converting the G-q curve graphs at different moments into G-f curve graphs; wherein f is the ratio of the deleted connecting edge to the total number of edges of the urban traffic flow network;
and the health state determining unit of the urban traffic flow network is used for determining the health states of the urban traffic flow network at different moments according to the G-f curve graphs at different moments.
9. The traffic network health representation system based on fuzzy clustering of claim 8, wherein the segmentation result determination module specifically comprises:
the clustering feature vector determining unit is used for determining clustering feature vectors at corresponding moments according to G-f curve graphs at different moments;
and the division result determining unit is used for carrying out fuzzy clustering on the characteristic vectors by utilizing an FCM clustering algorithm to divide the health state of the urban traffic flow network into healthy state or unhealthy state.
10. The traffic network health representation system based on fuzzy clustering of claim 6, wherein the health representation module specifically comprises:
the index determining unit is used for respectively determining network structure indexes and network operation indexes of a healthy urban traffic flow network and an unhealthy urban traffic flow network; the network structure indicators include: average degree and dynamic betweenness; the network operation indicators include: TTI index and network average speed;
and the health portrait unit is used for respectively portraying the health of the corresponding urban traffic flow network by the network structure index and the network operation index.
Background
In recent years, with the increasing of climate change, extreme disaster events such as typhoon, rainstorm, epidemic situation, earthquake and other sudden events occur continuously, research on urban traffic toughness has very important theoretical value and practical significance, and relevant expressions such as 'strengthening urban disaster coping capability and improving urban toughness' are provided in a new round of urban overall planning of cities such as Beijing, Shanghai and the like. The emergent epidemic crisis in the year, the more perceptual knowledge of city toughness is given, and the more rational thinking is given to how cities normally operate and maintain toughness in the increasing and unrealistic risks and challenges. The occurrence of an emergency brings huge impact and influence on the definition and use of the current urban space. Emergencies continuously remold cities and society and indirectly promote the birth and the evolution of modern city planning. During epidemic situations, the city service and supply mode are changed on line, and the functional form and use mode of the spaces such as city living, employment and traffic are redefined. The urban traffic network is used as a lifeline of a 'flexible city', national development, social security guarantee and guarantee of quality of life of people are achieved, and the health level of the traffic network can reflect the comprehensive ability of the urban network in maintaining daily operation and responding to emergencies, so that monitoring and evaluating the health level of the urban traffic network becomes an important task.
The traffic health portrait is the basis of the evaluation of the health status of the traffic network. Previous traffic health assessments were primarily based on a sum or average of statistical measures for each link, such as average speed, average travel delay time, average distance to congested links, and the like. However, since the urban traffic system has high complexity and coupling, these statistical-based measurements rarely consider the coupling relationship between road performances, and cannot reasonably evaluate the health status of the traffic network from the perspective of the overall network, which affects the accuracy of the health evaluation of the traffic network. On the other hand, research has been carried out to describe the overall operation state of a traffic network based on an analysis method of seepage, and the description can be used as a health assessment index of an urban traffic network.
In addition, a great deal of cognitive uncertainty exists in traffic system assessment, the cognitive uncertainty is subjective, and is generally caused by the complexity of the system, the incompleteness and ambiguity of information, the lack of human cognition and the like, so that the essential characteristics of things cannot be mastered or understood within a certain time range, and the characteristics of serious 'large data volume and poor knowledge' exist. The previous definition of the health level of the traffic network is mainly based on the artificial definition of an index interval, and the traffic network usually has strong subjectivity and cannot well reflect the objective mechanism of objects.
Therefore, the problem of strong subjectivity can be effectively solved by mining the road network health mechanism from the big data, researchers can carry out statistical analysis on historical data of the road network by applying a statistical mathematical theory and a statistical method at present, real-time running characteristics of the traffic road network are mined, and influences of subjective factors on road network evaluation are reduced, but the existing statistical analysis method cannot divide the road network into health states according to the characteristics.
Disclosure of Invention
The invention aims to provide a traffic network health portrait method and system based on fuzzy clustering, which can accurately classify the health state of a traffic network so as to solve the problem of urban road network health portrait.
In order to achieve the purpose, the invention provides the following scheme:
a traffic network health image method based on fuzzy clustering comprises the following steps:
obtaining vehicle road information in set time in a road network region to be analyzed; the vehicle road information includes: the road number, the road starting point number, the road end point number, the road grade, the road information, the road vehicle speed and the longitude and latitude information at each moment;
preprocessing the vehicle road information, and constructing an urban traffic flow network of the road network region to be analyzed according to the preprocessed data;
determining the health state of the urban traffic flow network at different moments by adopting a seepage analysis method;
dividing the health states of the urban traffic flow network at different moments by adopting a clustering analysis method;
and performing health portrayal on the urban traffic flow network of the road network area to be analyzed by using the division result.
Optionally, the preprocessing the vehicle road information and constructing the urban traffic flow network of the road network region to be analyzed according to the preprocessed data specifically include:
matching the road information with the speed of the road vehicle at each moment; completing the speed of the missing road vehicle;
matching the road starting point number, the road end point number and the longitude and latitude;
and constructing the urban traffic flow network by taking the roads in the road network area to be analyzed as the connecting edges of the urban traffic flow network, taking the intersections as the nodes of the urban traffic flow network and taking the real-time relative vehicle speed of each road as the weight of the connecting edges.
Optionally, the determining the health state of the urban traffic flow network at different times by using a seepage analysis method specifically includes:
acquiring a relative speed threshold; the relative speed threshold is a numerical value which is changed from 0 to 1 according to set precision;
deleting the connecting edges of each road in the urban traffic flow network, the real-time relative vehicle speed of which is less than the relative speed threshold value at the corresponding moment, and determining the maximum connected sub-cluster size of the urban traffic flow network at the corresponding moment;
determining G-q curve graphs at different moments according to the maximum connected sub-cluster size of the urban traffic flow network at each moment and the corresponding relative speed threshold; wherein G is the size of the maximum connected cluster, and q is a relative speed threshold;
converting G-q curve graphs at different moments into G-f curve graphs; wherein f is the ratio of the deleted connecting edge to the total number of edges of the urban traffic flow network;
and determining the health state of the urban traffic flow network at different moments according to the G-f curve graphs at different moments.
Optionally, the dividing the health state of the urban traffic flow network at different times by using a cluster analysis method specifically includes:
determining clustering characteristic vectors at corresponding moments according to G-f curve graphs at different moments;
and carrying out fuzzy clustering on the feature vectors by utilizing an FCM clustering algorithm to classify the health state of the urban traffic flow network into healthy or unhealthy.
Optionally, the performing the health representation of the urban traffic flow network of the road network region to be analyzed by using the division result specifically includes:
respectively determining network structure indexes and network operation indexes of healthy urban traffic flow networks and unhealthy urban traffic flow networks; the network structure indicators include: average degree and dynamic betweenness; the network operation indicators include: TTI index and network average speed;
the network structure index and the network operation index respectively carry out health portrayal on the corresponding urban traffic flow network.
A traffic network health representation system based on fuzzy clustering comprises:
the vehicle road information acquisition module is used for acquiring vehicle road information in set time in a road network region to be analyzed; the vehicle road information includes: the road number, the road starting point number, the road end point number, the road grade, the road information, the road vehicle speed and the longitude and latitude information at each moment;
the urban traffic flow network construction module is used for preprocessing the vehicle road information and constructing an urban traffic flow network of the road network area to be analyzed according to the preprocessed data;
the health state determining module of the urban traffic flow network is used for determining the health states of the urban traffic flow network at different moments by adopting a seepage analysis method;
the division result determining module is used for dividing the health states of the urban traffic flow network at different moments by adopting a clustering analysis method;
and the health portrait module is used for carrying out health portrait on the urban traffic flow network of the road network area to be analyzed by utilizing the division result.
Optionally, the urban traffic flow network construction module specifically includes:
a first matching unit for matching the road information with the road vehicle speed at each moment; completing the speed of the missing road vehicle;
the second matching unit is used for matching the road starting point number, the road end point number and the longitude and latitude;
and the urban traffic flow network construction unit is used for constructing the urban traffic flow network by taking the roads in the road network area to be analyzed as the connecting edges of the urban traffic flow network, taking the intersections as the nodes of the urban traffic flow network and taking the real-time relative vehicle speed of each road as the connecting edge weight.
Optionally, the health status determining module of the urban traffic flow network specifically includes:
a relative speed threshold value acquisition unit for acquiring a relative speed threshold value; the relative speed threshold is a numerical value which is changed from 0 to 1 according to set precision;
the maximum connected sub-cluster size determining unit is used for deleting the connected edges of each road in the urban traffic flow network, of which the real-time relative vehicle speed is smaller than the relative speed threshold at the corresponding moment, and determining the maximum connected sub-cluster size of the urban traffic flow network at the corresponding moment;
the G-q curve graph determining unit is used for determining G-q curve graphs at different moments according to the maximum connected sub-cluster size of the urban traffic flow network at each moment and the corresponding relative speed threshold; wherein G is the size of the maximum connected cluster, and q is a relative speed threshold;
the conversion unit is used for converting the G-q curve graphs at different moments into G-f curve graphs; wherein f is the ratio of the deleted continuous edge to the total number of edges of the urban traffic flow network
And the health state determining unit of the urban traffic flow network is used for determining the health states of the urban traffic flow network at different moments according to the G-f curve graphs at different moments.
Optionally, the dividing result determining module specifically includes:
the clustering feature vector determining unit is used for determining clustering feature vectors at corresponding moments according to G-f curve graphs at different moments;
and the division result determining unit is used for carrying out fuzzy clustering on the characteristic vectors by utilizing an FCM clustering algorithm to divide the health state of the urban traffic flow network into healthy state or unhealthy state.
Optionally, the health representation module specifically includes:
the index determining unit is used for respectively determining network structure indexes and network operation indexes of a healthy urban traffic flow network and an unhealthy urban traffic flow network; the network structure indicators include: average degree and dynamic betweenness; the network operation indicators include: TTI index and network average speed;
and the health portrait unit is used for respectively portraying the health of the corresponding urban traffic flow network by the network structure index and the network operation index.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the traffic network health image method and system based on fuzzy clustering provided by the invention can be used for analyzing and describing the states of the traffic network through traffic seepage and performing health classification on the states of the traffic network by using the fuzzy clustering method, thereby solving the problem of urban network health image. The fuzzy clustering method is adopted to solve the problem that the road network health boundary division is not clear, two types of networks, namely a healthy road network and an unhealthy road network, are mined from big data, and the difference of the healthy network and the unhealthy network in characteristic indexes is determined, so that the health image function of the road network is realized, and a foundation is laid for the subsequent traffic network health assessment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a traffic network health representation method based on fuzzy clustering according to the present invention;
FIG. 2 is a schematic view of the health status of an urban traffic flow network at different times;
FIG. 3 is a schematic diagram of a characteristic index of an urban traffic flow network;
FIG. 4 is a schematic diagram of a traffic network health representation system based on fuzzy clustering according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a traffic network health portrait method and system based on fuzzy clustering, which can accurately classify the health state of a traffic network so as to solve the problem of urban road network health portrait.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow chart of a traffic network health image method based on fuzzy clustering provided by the present invention, and as shown in fig. 1, the traffic network health image method based on fuzzy clustering provided by the present invention includes:
s101, obtaining vehicle road information in set time in a road network region to be analyzed; the vehicle road information includes: the road number, the road starting point number, the road end point number, the road grade, the road information, the road vehicle speed and the longitude and latitude information at each moment;
s102, preprocessing the vehicle road information, and constructing an urban traffic flow network of the road network region to be analyzed according to preprocessed data;
s102 specifically comprises the following steps:
matching the road information with the speed of the road vehicle at each moment; completing the speed of the missing road vehicle; the missing phenomenon of the acquired speed data is caused by the fact that some road sections lack arrangement of related data acquisition equipment and vehicles do not pass through the data acquisition equipment of the road at some time, and the speed data is compensated for, so that the missing speed is compensated.
Matching the road starting point number, the road end point number and the longitude and latitude;
and constructing the urban traffic flow network by taking the roads in the road network area to be analyzed as the connecting edges of the urban traffic flow network, taking the intersections as the nodes of the urban traffic flow network and taking the real-time relative vehicle speed of each road as the weight of the connecting edges.
Wherein the content of the first and second substances,
vcrepresents a relative velocity; v denotes the road actual speed, vmaxIndicating the maximum speed of the road over a specified time.
S103, determining the health states of the urban traffic flow network at different moments by adopting a seepage analysis method;
s103 specifically comprises the following steps:
acquiring a relative speed threshold; the relative speed threshold is a numerical value which is changed from 0 to 1 according to set precision; the setting accuracy is as 0.1.
Deleting the connecting edges of each road in the urban traffic flow network, the real-time relative vehicle speed of which is less than the relative speed threshold value at the corresponding moment, and determining the maximum connected sub-cluster size of the urban traffic flow network at the corresponding moment;
determining G-q curve graphs at different moments according to the maximum connected sub-cluster size of the urban traffic flow network at each moment and the corresponding relative speed threshold; wherein G is the size of the maximum connected cluster, and q is a relative speed threshold;
i.e. each time v is deletedcAnd (3) calculating the maximum connected sub-cluster size G of the road network under the deleted connected side, and finally obtaining a G-q curve graph at each moment through multiple seepage of the road network under each moment.
Converting G-q curve graphs at different moments into G-f curve graphs; wherein f is the ratio of the deleted connecting edge to the total number of edges of the urban traffic flow network.
The edge-deletion ratio f, i.e. v, at each relative velocity threshold qcAnd the number of the connecting edges smaller than q is in proportion to the number of the connecting edges of the whole road network, so that the G-q curve graph is converted into a G-f curve graph, and the health state of the road network at the moment is described by using the G-f curve at each moment.
And determining the health state of the urban traffic flow network at different moments according to the G-f curve graphs at different moments.
S104, dividing the health states of the urban traffic flow network at different moments by adopting a clustering analysis method;
s104 specifically comprises the following steps:
determining clustering characteristic vectors at corresponding moments according to G-f curve graphs at different moments;
as a specific embodiment, the area of each G-f curve from the x axis is calculated to serve as the clustering feature vector of the network at each moment.
And carrying out fuzzy clustering on the feature vectors by utilizing an FCM clustering algorithm to classify the health state of the urban traffic flow network into healthy or unhealthy.
Specifically, the divided G-f curves are labeled, wherein the G-f curve is first in a descending trend and is in an unhealthy state, and the other G-f curve is in a healthy state, as shown in fig. 2.
And S105, performing health representation on the urban traffic flow network of the road network area to be analyzed by using the division result.
S105 specifically comprises the following steps:
network structure indexes and network operation indexes of healthy urban traffic flow networks and unhealthy urban traffic flow networks are respectively determined and are shown in fig. 3; the network structure index includes but is not limited to average degree and dynamic betweenness; the network operation indicators include, but are not limited to, TTI index and network average speed; namely, characteristic indexes which can be used for evaluating the efficiency of the traffic network are proposed and calculated from multiple angles.
The network structure index and the network operation index respectively carry out health portrayal on the corresponding urban traffic flow network.
And respectively drawing a characteristic index membership degree change graph along with the health type, different types of characteristic index boxline graphs and a characteristic index change graph along with time, and describing the difference of the two types of networks on the characteristic index from multiple angles, thereby describing the characteristics of the healthy state and the unhealthy state of the urban traffic network and realizing the healthy portrait.
The difference of the healthy network and the unhealthy network in the characteristic indexes is described by calculating the structural indexes, the operation indexes and other related characteristic indexes of the urban traffic network, so that the healthy image function of the road network is realized, and a foundation is laid for the subsequent health assessment of the traffic road network.
Fig. 4 is a schematic structural diagram of a traffic network health representation system based on fuzzy clustering, as shown in fig. 4, the traffic network health representation system based on fuzzy clustering provided by the present invention includes:
a vehicle road information obtaining module 401, configured to obtain vehicle road information within a set time in a road network region to be analyzed; the vehicle road information includes: the road number, the road starting point number, the road end point number, the road grade, the road information, the road vehicle speed and the longitude and latitude information at each moment;
an urban traffic flow network construction module 402, configured to preprocess the vehicle road information, and construct an urban traffic flow network of the road network region to be analyzed according to the preprocessed data;
a health status determination module 403 of the urban traffic flow network, configured to determine health statuses of the urban traffic flow network at different times by using a seepage analysis method;
a partitioning result determining module 404, configured to partition the health states of the urban traffic flow network at different times by using a clustering analysis method;
and a health portrait module 405, configured to portrait health of the urban traffic flow network of the road network area to be analyzed by using the partitioning result.
The urban traffic flow network construction module 402 specifically includes:
a first matching unit for matching the road information with the road vehicle speed at each moment; completing the speed of the missing road vehicle;
the second matching unit is used for matching the road starting point number, the road end point number and the longitude and latitude;
and the urban traffic flow network construction unit is used for constructing the urban traffic flow network by taking the roads in the road network area to be analyzed as the connecting edges of the urban traffic flow network, taking the intersections as the nodes of the urban traffic flow network and taking the real-time relative vehicle speed of each road as the connecting edge weight.
The health status determination module 403 of the urban traffic flow network specifically includes:
a relative speed threshold value acquisition unit for acquiring a relative speed threshold value; the relative speed threshold is a numerical value which is changed from 0 to 1 according to set precision;
the maximum connected sub-cluster size determining unit is used for deleting the connected edges of each road in the urban traffic flow network, of which the real-time relative vehicle speed is smaller than the relative speed threshold at the corresponding moment, and determining the maximum connected sub-cluster size of the urban traffic flow network at the corresponding moment;
the G-q curve graph determining unit is used for determining G-q curve graphs at different moments according to the maximum connected sub-cluster size of the urban traffic flow network at each moment and the corresponding relative speed threshold; wherein G is the size of the maximum connected cluster, and q is a relative speed threshold;
the conversion unit is used for converting the G-q curve graphs at different moments into G-f curve graphs; wherein f is the ratio of the deleted connecting edge to the total number of edges of the urban traffic flow network;
and the health state determining unit of the urban traffic flow network is used for determining the health states of the urban traffic flow network at different moments according to the G-f curve graphs at different moments.
The dividing result determining module 404 specifically includes:
the clustering feature vector determining unit is used for determining clustering feature vectors at corresponding moments according to G-f curve graphs at different moments;
and the division result determining unit is used for carrying out fuzzy clustering on the characteristic vectors by utilizing an FCM clustering algorithm to divide the health state of the urban traffic flow network into healthy state or unhealthy state.
The health representation module 405 specifically includes:
the index determining unit is used for respectively determining network structure indexes and network operation indexes of a healthy urban traffic flow network and an unhealthy urban traffic flow network; the network structure indicators include: average degree and dynamic betweenness; the network operation indicators include: TTI index and network average speed;
and the health portrait unit is used for respectively portraying the health of the corresponding urban traffic flow network by the network structure index and the network operation index.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
