CN111080110A - Airport congestion risk analysis system - Google Patents
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Abstract
The invention relates to an airport congestion risk analysis system. The airport congestion risk analysis system comprises: the model building module, the congestion leg calculation module, the parameter building module and the congestion risk calculation module are suitable for analyzing the congestion risk of each waypoint in the busy airport terminal area airspace network topological structure model according to the leg congestion performance index LCP and the structural vulnerability index TVI. By combining the network characteristics of the current busy airport terminal airspace, the method can more scientifically and reasonably identify and predict the congestion risk of the busy airport terminal area, can find out the weak part of the airport airspace network structure construction, and has certain guiding significance for the optimization planning of the future airline network.
Description
Technical Field
The invention relates to the field of aviation, in particular to an airport congestion risk analysis system.
Background
The air traffic jam analysis is the basis and premise for preventing and managing air traffic jam, through discussing and exploring an airport jam risk analysis system, the research result promotes the optimization of an air traffic jam management system of a domestic busy airport, the prediction efficiency and the prediction accuracy are improved, the air traffic jam state in the peak time period of the predicted flow is timely and effectively recognized through a recognition system, and the frequent occurrence of the jam in the peak time period of the busy airport flow is avoided as much as possible, so that the pressure brought to a control department by the huge flow is reduced, the safety and the circulation of the regional air route operation of the busy airport are improved, the entering and leaving operation efficiency in the peak time of the flow is increased, the operation cost of an airline company is reduced, and the contradiction between capacity and demand is prevented as much as possible to become a short board for development of tens of millions of.
How to enrich the short board of the existing congestion risk analysis in the aspect of network angle research is urgent to be solved at present.
Disclosure of Invention
The invention aims to provide an airport congestion risk analysis system.
In order to solve the above technical problem, the present invention provides an airport congestion risk analysis system, including:
the model building module is suitable for building a busy airport terminal area airspace network topological structure model;
the congestion leg calculation module is suitable for calculating a congestion leg in an airspace network topological structure model of a busy airport terminal area according to a leg congestion performance index LCP;
the parameter construction module is suitable for carrying out structural feature analysis on route points connected with the congested route sections and constructing three parameters of a network topological structure comprising a value, a betweenness and a clustering coefficient;
the vulnerability index calculation module is suitable for calculating a structural vulnerability index TVI of the route point according to the value, the betweenness and the clustering coefficient;
and the congestion risk calculation module is suitable for analyzing the congestion risk of each route point in the airspace network topological structure model of the terminal area of the busy airport according to the section congestion performance index LCP and the structural vulnerability index TVI.
The invention has the beneficial effect that the invention provides an airport congestion risk analysis system. The method comprises the following steps: the model building module is suitable for building a busy airport terminal area airspace network topological structure model; the congestion leg calculation module is suitable for calculating a congestion leg in an airspace network topological structure model of a busy airport terminal area according to a leg congestion performance index LCP; the parameter construction module is suitable for carrying out structural feature analysis on route points connected with congested route sections and constructing three parameters of a network topological structure comprising a degree value, a betweenness and a clustering coefficient; the vulnerability index calculation module is suitable for calculating a structural vulnerability index TVI of the waypoint according to the value, the betweenness and the clustering coefficient; and the congestion risk calculation module is suitable for analyzing the congestion risk of each route point in the airspace network topological structure model of the terminal area of the busy airport according to the section congestion performance index LCP and the structural vulnerability index TVI. By combining the network characteristics of the current busy airport terminal airspace, the method can more scientifically and reasonably identify and predict the congestion risk of the busy airport terminal area, can find out the weak points of the airport airspace network structure construction, and has certain guiding significance for the optimization planning of the future airline network.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a schematic flow chart of an airport congestion risk analysis system provided by the present invention.
FIG. 2 is a schematic structural diagram of a busy airport terminal area airspace network topology model provided by the present invention.
Fig. 3 is a schematic view of the airport terminal airspace network structure provided in embodiment 2.
Fig. 4 is a transacad leg data input interface.
Fig. 5 is a schematic diagram of centroid point selection.
Fig. 6 is a row distribution matrix.
Fig. 7 is a flow rate distribution diagram.
Fig. 8 is a schematic diagram of node and leg data import.
FIG. 9 is a key waypoint structure diagram.
Fig. 10 is a map of the correspondence between road congestion conditions and segment congestion condition indexes.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
Example 1
As shown in fig. 1, the present embodiment 1 provides an airport congestion risk analysis system. By combining the network characteristics of the terminal airspace of the busy airport, the method can more scientifically and reasonably identify and predict the congestion risk of the terminal area of the busy airport, can find out the weak part of the network structure construction of the airport airspace, and has certain guiding significance for the optimization planning of the future airline network. Wherein, airport congestion risk analysis system includes:
the model building module is suitable for building a busy airport terminal area airspace network topological structure model;
the congestion leg calculation module is suitable for calculating a congestion leg in an airspace network topological structure model of a busy airport terminal area according to a leg congestion performance index LCP;
the parameter construction module is suitable for carrying out structural feature analysis on route points connected with the congested route sections and constructing three parameters of a network topological structure comprising a value, a betweenness and a clustering coefficient;
the vulnerability index calculation module is suitable for calculating a structural vulnerability index TVI of the route point according to the value, the betweenness and the clustering coefficient;
and the congestion risk calculation module is suitable for analyzing the congestion risk of each route point in the airspace network topological structure model of the terminal area of the busy airport according to the section congestion performance index LCP and the structural vulnerability index TVI.
In this embodiment, the busy airport terminal area airspace network includes two basic elements, namely, waypoints and a route, and in the airport terminal area, a route passes through a plurality of waypoints which may be navigation stations, report points, route intersections, and the like with different properties. A model establishing module suitable for establishing a busy airport terminal area airspace network topological structure model, namely
A terminal area airspace network of a busy airport is described by adopting a complex network theory, each route point is represented by a node, a route between two route points is represented by an edge, if the route has no specified direction, the route is a non-directional edge, otherwise, the route is a directional edge, and therefore the airport terminal area airspace is abstracted into a complex network structure containing the node and the edge, namely a busy airport terminal area airspace network topological structure model. The busy airport terminal area airspace network topology model is shown in fig. 2.
In this embodiment, the congestion leg calculation module is adapted to calculate a congestion leg in the airspace network topology model of the terminal area of the busy airport, that is, a congestion leg in the congestion leg calculation module according to the leg congestion performance index LCP
The operation state of the airway is only related to two variables of airway traffic capacity C and required flow V, Z is used for representing the airway traffic capacity, and Z is C-V. Due to the instability of the operation of the airway, when the airway unit throughput is less than 0, it can be considered as a failure. Therefore, whether the airway unit is reliable can be described by the following 3 cases:
according to the cascade failure model, when the flow load of the nodes and the edges in the terminal area airspace network exceeds the capacity, namely V/C is larger than 1, the traffic jam occurs in the navigation section, so that the navigation section is in a failure state, and the traffic connection with the peripheral navigation paths is disconnected. Considering the difference of the traffic capacity of the air routes, when an evaluation index of network congestion is selected, the embodiment uses simulation software to realize simple traffic distribution of the whole terminal area air space network at the peak hour of a busy airport so as to evaluate the congestion risk of the airport terminal area air space network from the aspect of the operation state, and uses a drawing function in a simulation result to draw a ratio V/C result graph of traffic volume and capacity of traffic distribution and air routes of the whole network so as to evaluate the operation condition of the whole airport terminal area air space network. And (3) selecting an airway with a serious congestion condition to carry out independent airway congestion index calculation, wherein the calculation formula is as follows:
wherein i and j are respectively the initial point nodes of the road section; TCIijIs the traffic congestion index for road segment ij; vijIs the service traffic volume through the road section ij; cijIs the road traffic capacity of section ij.
In this embodiment, the parameter construction module is adapted to perform structural feature analysis on route points connected to congested leg segments, and construct three parameters of a network topology structure including a value, an betweenness, and a clustering coefficient, that is, three parameters of the network topology structure
Because the waypoints are important in the safety guarantee of air transportation, the failure of the waypoints can bring great damage to the operation of the airspace network in the terminal area of the whole busy airport. At the same time, the amount of traffic passing through waypoints during peak hours is enormous. Therefore, the congestion risk of the busy airport terminal area airspace network needs to be evaluated from two aspects by combining the vulnerability of the congestion state and the topology structure of the navigation network, so that the situation is objective and comprehensive. In the embodiment, Gephi software is used for carrying out structural feature analysis on route points connected with congested route sections, and three parameters of a network topology structure including a degree value, a betweenness and a clustering coefficient are constructed. The values of the three are all in direct proportion to the importance of the node in the network, which means that the larger the value is, the larger the attack degree of the node on the network function after the node fails is, that is, once the node fails, the probability of the network congestion is increased, and the more serious the consequences are. And the vulnerability of the network topology structure is reflected from different aspects, so the three characteristic parameters are adopted to construct a congestion risk evaluation formula.
In this embodiment, the vulnerability index calculation module is adapted to calculate the structural vulnerability index TVI of the waypoint in the structural vulnerability index TVI of the waypoint according to the values, the betweenness, and the clustering coefficients, and has the following calculation formula:
wherein k isiIs the value of node i; biIs the betweenness of the node i; ciIs the clustering coefficient of the node i; n is the number of nodes in the network; a isijIs the corresponding value in the adjacency matrix; dkj(i) Is the shortest path number passing through the node i; dkjThe shortest path number for nodes k and j; eiTo K connected to node iiThe number of edges actually present between the nodes.
In this embodiment, the congestion risk calculation module includes:
the network congestion risk calculation formula acquisition unit is suitable for acquiring a network congestion risk calculation formula;
and the route point congestion risk calculation formula obtaining unit is suitable for substituting the section congestion performance index LCP and the structural vulnerability index TVI into the network congestion risk calculation formula so as to obtain the route point congestion risk calculation formula.
The network congestion risk calculation formula is as follows:
Wa=λ1×qa+λ2×Ca;
wherein, WaThe overall congestion risk for waypoint a; lambda [ alpha ]1And λ2Respectively as indexes q of traffic running conditionsaAnd network structure index CaThe weight of (c).
The route point congestion risk calculation formula obtaining unit is suitable for substituting the section congestion performance index LCP and the structural vulnerability index TVI into the network congestion risk calculation formula so as to obtain a route point congestion risk calculation formula, namely
Selecting a section congestion performance index LCP as a traffic operation index, selecting a route point topological structure vulnerability index TVI as a network structure index, substituting the route point topological structure vulnerability index TVI into a network congestion risk calculation formula, and finally obtaining a route point overall congestion risk evaluation formula:
Wa=λ1×LCP+λ2×TVI。
in this embodiment, the weight λ of the index of the traffic operating condition in the route point congestion risk calculation formula obtaining unit10.6, weight of network structure index λ20.4, integrating a structural vulnerability index TVI calculation formula and a segment congestion performance index LCP calculation formula of the waypoint and substituting the structural vulnerability index TVI calculation formula and the segment congestion performance index LCP calculation formula into an overall congestion risk evaluation formula W of the waypointa=λ1×LCP+λ2Multiplying TVI, so as to obtain a final route point overall congestion risk evaluation formula as follows:
example 2
In this embodiment 2, an airport is studied according to the airport congestion risk analysis system provided in embodiment 1.
Specifically, according to a route map of an approaching area of a certain airport which is latest in 2019, route points are abstracted into nodes, routes are abstracted into edges, and the network topology structure is constructed on a TransCAD software, namely, a certain airport terminal area airspace network with 37 nodes, 58 undirected edges and 1 directed edge is formed, as shown in FIG. 3.
Various information is entered in the TransCAD software for traffic distribution, including the length of the leg, the speed of the aircraft, and the route capacity. The length of the flight section can be obtained according to a navigation map scale, and the speed and the traffic capacity of the air vehicle cannot be quantized into an accurate value in reality. Because the uncertainty factor of the real situation is very high. The types and the speeds of the aircrafts cannot be unified, and the safety intervals are different according to different conditions. Due to limited time and capability, embodiments select a large probability of data to set in order to simplify the model. The speed of the aircraft in the area near the airport is set to be 250-500km/h, the interval is set to be 10-20km, the rest area is set to be 600-900km/h, and the interval is set to be 20 km. The air route traffic capacity is obtained according to a formula, the peak time of a certain airport is 10-11 points, 14-16 points and 18-21 points, the peak hour capacity is 45 frames, and therefore the air route traffic capacity is not higher than 45 frames per hour. The formula of the passage capacity is as follows:
wherein: t is t0Is the minimum time interval(s) of the machine head0Is the minimum head spacing (m) and v is the aircraft speed (km/h). The specific leg data is entered as shown in fig. 4.
And then marking a center of mass point according to the course trend as a source of the flow. Since the analysis presupposes that an airport is a take-off and landing airport, traffic sources include waypoints in different directions and also the airport itself. A schematic diagram of center of mass point selection is shown in fig. 5.
And then establishing a travel OD matrix according to the cell centroid points established in the previous step, and performing matrix back-stepping to obtain a travel distribution matrix. The travel distribution matrix is shown in fig. 6.
Finally, simple traffic distribution is carried out on the TransCAD, and a flow distribution diagram is obtained and is shown in FIG. 7.
According to the flow distribution diagram, the distribution of the network congestion condition of the airspace in a terminal area of an airport can be visually seen. Most of the routes have smooth running conditions and are generally distributed at sparse nodes. And the congestion of a small part of routes is serious, and the routes are generally distributed at the dense positions of nodes. The method shows that the traffic flow of the place with dense nodes in the network is large, the probability of congestion is high, the traffic congestion is easy to occur once an accident occurs, and the congestion is easy to spread to the adjacent nodes to cause the congestion to spread.
Five sections of routes have serious congestion, which respectively are as follows: in the voyage 31, the V/C value is 0.76; in the leg section 5, the V/C value is 1.14; in the flight 27, the V/C value is 0.76, and in the flight 19, the V/C value is 0.93; and in the range of 28, the V/C value is 0.75. One of the routes is already in a failure state because the V/C ratio is larger than 1. The section congestion indexes TCI are 4, 4, 4, 5, and 4, respectively, and the correspondence between the road congestion condition and the section congestion condition index is shown in fig. 10.
in analyzing the congestion risk based on the vulnerability of the topological structure, firstly, the node data and the side data of the airspace network topological structure of a certain airport terminal area are imported into Gephi software to generate a complex network diagram, and the network characteristic parameters are calculated, and the node and the flight segment data are imported as shown in FIG. 8.
According to calculation of Gephi software, the average degree of the network overall characteristic parameters is 3.189, the average clustering coefficient is 0.241, and the average path length is 3.458. On average, each node is connected with 3 edges, which shows that the connection of the whole network is relatively sparse, the air routes have certain substitutability, and when a certain air route is congested, the traffic on the air route can be transferred to other air routes, but the traffic volume can be born is not much. The average path length of the network is relatively large, so the network performance and the network efficiency are relatively low, and the smoothness of the air route is easily influenced. The average cluster coefficient is lower, the accessibility is not high, the accessibility of the air route is not high, and the network structure is slightly single. Generally speaking, as a busy airport, the performance of a certain airport network is poor, so the structure vulnerability is high, the probability of congestion at peak time is high, and the consequences are serious once congestion occurs. The traffic at peak time is huge, once the airport airspace network structure cannot transfer well and efficiently, congestion is easy to occur, the congestion dissipation time is long, the consequence is serious, and the risk index is high. And the proposal enriches the network structure and increases the hub nodes to bear the traffic transmission work. The key waypoints are shown in fig. 9.
After the network operation congestion condition and the overall structure congestion risk are analyzed, local key route points are analyzed. And extracting nodes at two ends of the navigation section with serious congestion, and finding out respective network structure characteristic values of the nodes, including node degrees, betweenness centrality degrees and clustering coefficients on the software operation result. As shown in the table below.
Node characteristic value of congested flight segment
And finally, calculating according to the data:
acquiring the integral congestion risk of the waypoints of the key points:
W21=0.6×LCP31+0.4×TVI21=0.6×3.04+0.4×45.61=20.068
W6=0.6×LCP5+0.4×TVI6=0.6×1.14+0.4×51.24=21.18
W16=0.6×LCP19+0.4×TVI16=0.6×0.93+0.4×21.1=9
the influence of the nodes 5 and 6, namely UGAGO waypoints and Tung cottage waypoints, on the airport terminal area airspace network is the largest, the association degree with the whole network is larger, and the nodes can be called as junction waypoints, so that the vulnerability is higher, and the congestion risk is higher at the peak time. Once this waypoint fails, the impact on the overall network can be significant. Without a hub route for transferring and evacuating the peak traffic, the probability of network congestion is increased, the congestion is difficult to evacuate, and the loss and the consequence are serious due to long evacuation time. The nodes 21 and 23, namely the purslane bridge and the Party mountain route, affect the network degree secondarily, because they are close to the airport, the influence on the safety efficiency of the entering and leaving flight operation at the peak time of the airport is the largest, the congestion risk is also large, the failure effect is obvious, the probability of the congestion at the peak time of the airport is increased, once the congestion occurs, the entering and leaving of the flight is delayed in a large area, even some flights are forced to descend, and then huge economic losses are brought to the airport and related air carriers. Therefore, the airport should pay more attention to and manage the waypoints, so that the stable operation of the navigation station is guaranteed, and the airport has a positive effect on the smoothness of network operation.
In summary, the invention provides an airport congestion risk analysis system. The method comprises the following steps: the model building module is suitable for building a busy airport terminal area airspace network topological structure model; the congestion leg calculation module is suitable for calculating a congestion leg in the airspace network topological structure model of the terminal area of the busy airport according to the leg congestion performance index LCP; the parameter construction module is suitable for carrying out structural feature analysis on route points connected with the congested route sections and constructing three parameters of a network topological structure comprising a value, a betweenness and a clustering coefficient; the vulnerability index calculation module is suitable for calculating a structural vulnerability index TVI of the route point according to the value, the betweenness and the clustering coefficient; and the congestion risk calculation module is suitable for analyzing the congestion risk of each route point in the airspace network topological structure model of the terminal area of the busy airport according to the section congestion performance index LCP and the structural vulnerability index TVI. By combining the network characteristics of the current busy airport terminal airspace, the method can more scientifically and reasonably identify and predict the congestion risk of the busy airport terminal area, can find out the weak part of the airport airspace network structure construction, and has certain guiding significance for the optimization planning of the future airline network.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (8)
1. An airport congestion risk analysis system, comprising:
the model building module is suitable for building a busy airport terminal area airspace network topological structure model;
the congestion leg calculation module is suitable for calculating a congestion leg in an airspace network topological structure model of a busy airport terminal area according to a leg congestion performance index LCP;
the parameter construction module is suitable for carrying out structural feature analysis on route points connected with the congested route sections and constructing three parameters of a network topological structure comprising a value, a betweenness and a clustering coefficient;
the vulnerability index calculation module is suitable for calculating a structural vulnerability index TVI of the route point according to the value, the betweenness and the clustering coefficient;
and the congestion risk calculation module is suitable for analyzing the congestion risk of each route point in the airspace network topological structure model of the terminal area of the busy airport according to the section congestion performance index LCP and the structural vulnerability index TVI.
2. The airport congestion risk analysis system of claim 1, wherein the model building module is adapted to build a model of a busy airport terminal airspace network topology, i.e., a model of a busy airport terminal airspace network topology
A terminal area airspace network of a busy airport is described by adopting a complex network theory, each route point is represented by a node, a route between two route points is represented by an edge, if the route has no specified direction, the route is a non-directional edge, otherwise, the route is a directional edge, and therefore the airport terminal area airspace is abstracted into a complex network structure containing the node and the edge, namely a busy airport terminal area airspace network topological structure model.
3. The airport congestion risk analysis system of claim 2,
the section congestion performance index LCP in the congested section calculation module has the following calculation formula:
wherein i and j are respectively the initial point nodes of the road section; TCIijIs the traffic congestion index for road segment ij; vijIs the service traffic volume through the road section ij; cijIs the road traffic capacity of section ij.
4. The airport congestion risk analysis system of claim 3, wherein the vulnerability index calculation module is adapted to calculate the structural vulnerability index TVI of the waypoint according to the values, medians and clustering coefficients, namely:
the structural vulnerability index TVI of the waypoint has the calculation formula as follows:
wherein k isiIs the value of node i; biIs the betweenness of the node i; ciIs the clustering coefficient of the node i; n is the number of nodes in the network; a isijIs the corresponding value in the adjacency matrix; dkj(i) Is the shortest path number passing through the node i; dkjThe shortest path number for nodes k and j; eiTo K connected to node iiThe number of edges actually present between the nodes.
5. The airport congestion risk analysis system of claim 4, wherein the congestion risk calculation module comprises:
the network congestion risk calculation formula acquisition unit is suitable for acquiring a network congestion risk calculation formula;
and the route point congestion risk calculation formula obtaining unit is suitable for substituting the section congestion performance index LCP and the structural vulnerability index TVI into the network congestion risk calculation formula so as to obtain the route point congestion risk calculation formula.
6. The airport congestion risk analysis system of claim 5, wherein the network congestion risk calculation formula is:
Wa=λ1×qa+λ2×Ca;
wherein, WaThe overall congestion risk for waypoint a; lambda [ alpha ]1And λ2Respectively as indexes q of traffic running conditionsaAnd network structure index CaThe weight of (c).
7. The airport congestion risk analysis system of claim 6,
the route point congestion risk calculation formula obtaining unit is suitable for substituting the section congestion performance index LCP and the structural vulnerability index TVI into the network congestion risk calculation formula so as to obtain a route point congestion risk calculation formula, namely
Selecting a section congestion performance index LCP as a traffic operation index, selecting an airway point topological structure vulnerability index TVI as a network structure index, substituting the network congestion risk calculation formula into the network congestion risk calculation formula, and finally obtaining an airway point overall congestion risk evaluation formula:
Wa=λ1×LCP+λ2×TVI。
8. the airport congestion risk analysis system of claim 7,
weight lambda of traffic operation condition index in route point congestion risk calculation formula acquisition unit10.6, weight of network structure index λ20.4, integrating a structural vulnerability index TVI calculation formula and a segment congestion performance index LCP calculation formula of the waypoints and substituting the structural vulnerability index TVI calculation formula and the segment congestion performance index LCP calculation formula into a waypoint overall congestion risk evaluation formula Wa=λ1×LCP+λ2Multiplying TVI, so as to obtain a final route point overall congestion risk evaluation formula as follows:
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Citations (6)
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---|---|---|---|---|
US20030041155A1 (en) * | 1999-05-14 | 2003-02-27 | Nelson Eric A. | Aircraft data communications services for users |
CN106251707A (en) * | 2016-08-16 | 2016-12-21 | 南京航空航天大学 | Airdrome scene jam level method for dynamically partitioning |
CN108683448A (en) * | 2018-04-24 | 2018-10-19 | 中国民航大学 | Influence power node recognition methods suitable for air net and system |
CN108898838A (en) * | 2018-08-03 | 2018-11-27 | 首都经济贸易大学 | A kind of aerodrome traffic congestion prediction technique and device based on LSTM model |
CN109637197A (en) * | 2019-01-21 | 2019-04-16 | 南京航空航天大学 | The busy grade stage division of way point based on probability density |
CN110188974A (en) * | 2019-04-01 | 2019-08-30 | 浙江交通职业技术学院 | A kind of subway transportation network vulnerability evaluation method |
-
2019
- 2019-12-09 CN CN201911247830.3A patent/CN111080110B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030041155A1 (en) * | 1999-05-14 | 2003-02-27 | Nelson Eric A. | Aircraft data communications services for users |
CN106251707A (en) * | 2016-08-16 | 2016-12-21 | 南京航空航天大学 | Airdrome scene jam level method for dynamically partitioning |
CN108683448A (en) * | 2018-04-24 | 2018-10-19 | 中国民航大学 | Influence power node recognition methods suitable for air net and system |
CN108898838A (en) * | 2018-08-03 | 2018-11-27 | 首都经济贸易大学 | A kind of aerodrome traffic congestion prediction technique and device based on LSTM model |
CN109637197A (en) * | 2019-01-21 | 2019-04-16 | 南京航空航天大学 | The busy grade stage division of way point based on probability density |
CN110188974A (en) * | 2019-04-01 | 2019-08-30 | 浙江交通职业技术学院 | A kind of subway transportation network vulnerability evaluation method |
Non-Patent Citations (3)
Title |
---|
张颖,胡明华,彭瑛: "多元受限空中交通流量管理决策支持系统", 《交通运输工程学报》 * |
杜婧涵,胡明华,张魏宁,尹嘉男: "基于度量学习的机场交通态势弱监督评估", 《北京航空航天大学学报》 * |
田文,胡明华: "空域拥挤风险管理时间决策模型与方法", 《南京航空航天大学学报》 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114782898A (en) * | 2022-05-09 | 2022-07-22 | 山东师范大学 | Crowd congestion prediction method and system based on knowledge graph and regional crowd density |
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