CN112071060A - Emergency rescue path planning method based on urban road network traffic environment change - Google Patents
Emergency rescue path planning method based on urban road network traffic environment change Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention discloses an emergency rescue path planning method based on urban road network traffic environment change, which comprises the following steps: 1) acquiring urban road network traffic environment change information and emergency rescue information; 2) analyzing urban emergency rescue characteristics, and determining emergency rescue path planning evaluation indexes; 3) constructing a basic framework for emergency rescue path planning collaborative optimization according to the urban road network traffic environment change information, emergency rescue information, urban emergency rescue characteristics and emergency rescue path planning evaluation indexes; 4) planning a basic framework of collaborative optimization according to the emergency rescue path, and providing a collaborative optimization algorithm; 5) and solving and outputting an emergency rescue path planning result by using a collaborative optimization algorithm. The method plans the optimal path based on the change of the urban road network traffic environment, can reduce the time of rescue journey, obtains the latest data of the urban road network traffic environment at intervals, predicts the change of the urban road network traffic environment again, adjusts the planned path, and improves the reliability of emergency rescue.
Description
Technical Field
The invention relates to the technical field of urban emergency rescue path planning, in particular to an emergency rescue path planning method based on urban road network traffic environment change.
Background
Emergency rescue is a key means for preventing further deterioration of the influence caused by emergencies (such as traffic accidents, fire disasters, medical accidents and the like) and ensuring the life safety of injured people. The emergency rescue efficiency is highly related to the rescue time, wherein the rescue travel time is the most critical, in other words, the rescue travel time determines the emergency rescue efficiency, the rescue travel time depends on the rescue path planning, and the emergency rescue path planning should shorten the travel time as much as possible, so the path planning is the most important loop in the emergency rescue response process.
In an actual scene, the state of the urban road network traffic environment is constantly changing, and the urban road network traffic environment refers to road network traffic flow speed, traffic flow density and the like. In order to shorten urban emergency rescue time and improve the reliability thereof as much as possible, the invention provides an emergency rescue path planning method based on urban road network traffic environment change, namely on the basis of predicting the urban road network traffic environment change, path planning and road network traffic environment change are optimized in a collaborative mode to plan a path with the shortest travel time; secondly, in the process of the rescue vehicle moving, the change of the road network traffic environment is predicted again at intervals according to the real-time state of the road network traffic environment, and the planned path is corrected; finally, a collaborative optimization algorithm is proposed to implement a collaborative optimization process. The method is suitable for urban emergency rescue scenes, namely path planning under the urban emergency rescue scenes is within the research range of the method, and reference and support can be provided for the urban emergency rescue path planning.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provides an emergency rescue path planning method based on urban road network traffic environment change.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: an emergency rescue path planning method based on urban road network traffic environment changes comprises the following steps:
1) acquiring urban road network traffic environment change information and emergency rescue information;
2) according to the change information of the urban road network traffic environment, analyzing urban emergency rescue characteristics and determining emergency rescue path planning evaluation indexes;
3) constructing a basic framework for emergency rescue path planning collaborative optimization according to the urban road network traffic environment change information, emergency rescue information, urban emergency rescue characteristics and emergency rescue path planning evaluation indexes;
4) planning a basic framework of collaborative optimization according to the emergency rescue path, and providing a collaborative optimization algorithm;
5) and solving and outputting an emergency rescue path planning result by using a collaborative optimization algorithm.
In step 1), the urban road network traffic environment change information comprises urban road network intersection node information, urban road network section length information, historical data and real-time data of urban road network traffic flow, average running speed of vehicles of each road network, historical information and real-time information of urban road network intersection node average traffic delay, and prediction information of average running speed of vehicles of each road network and average traffic delay of road network intersection nodes; the emergency rescue information comprises the geographical position of a rescue starting point and the geographical position of a rescue ending point.
The step 2) comprises the following steps:
2.1) analyzing the urban emergency rescue characteristics: urban emergency rescue is a key means for guaranteeing the life safety of injured people, so the emergency rescue aims to shorten rescue time as much as possible and improve emergency rescue efficiency, the emergency rescue efficiency is limited by rescue path travel time, and the reliability of the path travel time is a key for ensuring that rescue vehicles can arrive on time;
2.2) selecting evaluation indexes: according to the urban emergency rescue characteristic analysis, determining emergency rescue path planning evaluation indexes including path travel time and reliability thereof, introducing path length as a general index in path planning, wherein T is used for representing the path travel time, R is used for representing the path length, and T is used for representing the path travel time85％And R85％Reflecting reliability, wherein T85％An 85 quantile representing the path travel time, i.e. 85% of the path travel time is less than this value; r85％An 85 quantile representing the path length, i.e. 85% of the path length is less than this value.
In the step 3), a basic framework of collaborative optimization is to predict the future urban road network traffic flow state by using the current position as a new starting point through historical data of the urban road network traffic flow to obtain urban road network traffic environment change information, and then a path is planned on the basis, namely the planning process of the path is cooperatively performed with the urban road network traffic environment change; meanwhile, a fixed time step length T is setunitThe system is used for updating urban road network traffic flow data and urban road network traffic flow prediction information at regular time, and cooperatively optimizing the optimal path again according to the current position of the vehicle until the vehicle reaches a rescue terminal, in other words, the complete planning of the path is integrated by planning a plurality of sub-paths;
the collaborative optimization is a main objective of path travel time minimization:
in the formula (I), the compound is shown in the specification,
an optimal path, namely a sub-path, output at the moment t; p represents a set of sub-paths, and N (P) represents the number of sub-paths;
representing a path
A time cost function of; t represents the path travel time of the vehicle traveling along the sub-path set P; the objective function satisfies the following constraints:
I(P(i),P(i+1))＝1,i＝1,...,N(P)-1 (2)
wherein, I (P (I), P (I +1)) represents the connection state of the subpaths P (I) and P (I +1), wherein 1 represents connection, namely the starting point of P (I +1) is the end point of P (I), and 0 represents no connection;
representing a path
The number of included nodes;
representing a path
The ith node of (1);
indicating the node at the predicted time t + k
And node
The communication state between the two, wherein 1 represents communication, and 0 represents non-communication;
indicating the vehicle passing path at the predicted time t + k
Average delay of ith node;
indicating the vehicle passing path at the predicted time t + k
The travel time of a road section between the ith node and the (i +1) th node;
representing a path
A travel time cost function of;
the expressions (2) to (4) clearly indicate that the vehicle follows the path
The connectivity and the travel time of the sub-path are cooperatively changed along with time in the driving process, and the urban road network communication state and the urban road network traffic environment information of the sub-path at the predicted time t + k are obtained by dynamically evolving and predicting the urban road network traffic environment at the time t.
In step 4), the core idea of the collaborative optimization algorithm includes the following four parts:
the numerical attribute of the nodes: defining an F value attribute for each urban road network intersection node, wherein the calculation formula of the F value is as follows:
F(i)＝G(i)+H(i) (6)
in the formula, F (i) represents the value F of the node i, and the value F represents the movement cost of the current node to the end point of the road network via the node i; g (i) represents the G value of node i; h (i) represents the H value of node i; the G value represents the moving cost from the current node to the node i, and the H value represents the estimated cost from the node i to the road network terminal point;
selecting a node: defining l as the selection step number of the node, and when l is 1, selecting the node as the adjacent node of the current node; when l is more than or equal to 2, the selection steps are as follows:
a. defining one attribute of a node as 'whether the node is marked', if the node is selected, the attribute of the node is marked, otherwise, the node is not marked; meanwhile, defining a set M to represent a set of selected nodes, and putting the selected nodes when l is 1 into the set M;
b. for all nodes in the set M, selecting adjacent nodes of each node, taking all adjacent nodes with the attribute of 'unmarked' as a new set M, namely updating the nodes in the set M to be newly selected unmarked adjacent nodes, and simultaneously increasing the value of l by 1;
c. if the value of l reaches the set value, namely the set selection step number is reached, the node in the set M is the final selected node, otherwise, the step b is returned, and the current node cannot be selected; if the end point is marked in the selecting process, the end point is put into the set M, and all the following steps are skipped, namely only the end point exists in the set M;
and thirdly, combining the selection mode of the nodes mentioned in the second step, and calculating the G value by adopting a dynamic Dijkstra algorithm, wherein the core idea of the dynamic Dijkstra is as follows: calculating the path weight according to the prediction information of the urban road network traffic flow and the change of the urban road network traffic environment, namely the calculation process of the path weight and the change of the urban road network traffic environment are performed in a coordinated manner, firstly, an array Dis is stated to be used for storing the shortest travel time from a starting point to other nodes, and a set Q is used for storing the node with the shortest travel time; initially, only a starting point is in the set Q, the starting point is used as a current node, changes of urban road network traffic environment in a plurality of time units in the future are predicted according to the time of the current node, the predicted travel time between the current node and adjacent nodes of the current node and the predicted time of reaching each adjacent node are obtained, and the value of the corresponding node in the Dis is updated; then finding the minimum value and the corresponding node in the Dis, adding the minimum value and the corresponding node into the set Q, removing the minimum value and the corresponding node from the Dis, taking the node as the current node, and repeating the above process until all the nodes in the set M are added into the set Q, so that the predicted travel time, namely the value G, of all the selected nodes in the set M can be obtained; wherein the set M represents a set of selected nodes;
fourthly, estimating the H value by adopting a ripple diffusion algorithm, wherein the ripple diffusion algorithm has the following principle: assume a road network intersection node has four attributes: inactive, waiting, activated and dead; propagating initial ripples from a rescue starting point, wherein the ripples are diffused towards adjacent unactivated nodes along a road section, the diffusion speed is consistent with the predicted traffic flow speed of the road section in the current time unit, namely the diffusion process of the ripples is performed in coordination with the change of the urban road network traffic environment; when the ripple reaches an inactivated node, the node state is converted into a waiting state, and the waiting time is consistent with the average delay of the node; after the waiting time is over, activating the node to generate new ripples, wherein the node is activated at the moment; when all the adjacent nodes of the node are activated, the node is converted into a death state; the first ripple to reach the rescue terminal determines the path travel time.
In step 5), the operation steps of using the collaborative optimization algorithm are as follows:
5.1) calculating the optimal next node from the rescue starting point by using a collaborative optimization algorithm according to the F value of the node to obtain a path between the starting point and the node, namely a first sub-path;
5.2) the vehicle runs along the sub-path, and according to the actual road network traffic environment change condition, the travel time of the sub-path is obtained after the vehicle reaches the end point of the sub-path;
5.3) the end point of the sub-path is used as a new starting point, and on the basis of the actual time of reaching the node, the optimal next node is calculated by applying the collaborative optimization algorithm again to obtain the next sub-path;
5.4) repeating the step 5.2) until the rescue terminal is reached.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the emergency rescue path planning method provided by the invention has the advantage of cooperative optimization characteristics, can effectively avoid the upcoming congested area by predicting the change of the urban road network traffic environment, effectively shortens the rescue path travel time, is beneficial to reducing the emergency rescue time, improves the emergency rescue efficiency, and enables rescue vehicles to arrive at the accident site in the shortest time possible.
2. The emergency rescue path planning method provided by the invention integrates the advantages of Dijkstra algorithm and ripple diffusion algorithm, can reflect the actual operation condition of rescue vehicles, can estimate the path weight according to the change of urban road network traffic environment in the prediction process, can improve the precision and accuracy of the estimated value, and improves the reliability of path planning.
3. The method can be combined with a short-time traffic flow prediction method, carries out path planning on the prediction of the urban road network traffic environment, reduces the dependence on real-time urban road network traffic environment data, and can be applied to the urban road network with a longer data updating period or partial data loss.
4. The method has the advantages of local parallel computation, can effectively improve the storage efficiency of the computer, reduces the operation load, can quickly solve the optimal rescue path, and shortens the response time of emergency rescue.
5. The method has the advantages of re-optimization, when the vehicle operates for a certain distance, the planned path is corrected according to the real-time data of the urban road network traffic environment, the uncertainty of various actual conditions and the interference of calculation errors on the path planning can be effectively reduced, the accuracy of an output result is improved, the travel time of the output rescue path is shorter and more reliable, the rescue vehicle can reach a rescue site in the shortest time, the method is more efficient compared with other optimization methods, correct driving decisions such as temporary waiting or selecting detour can be made, the times of operating an algorithm can be reduced, and the calculation time of the optimal path is reduced.
6. The method is not only suitable for various scenes of urban emergency rescue, has important theoretical significance and social value for improving the efficiency and reliability of the urban emergency rescue, but also can be used for carrying out scene expansion, such as inter-city and even trans-provincial emergency rescue path planning, material scheduling path planning and the like, and has very high application value.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of a simulated road network structure according to the present invention.
Fig. 3 is a basic principle framework diagram of the path planning collaborative optimization of the present invention.
FIG. 4 is a basic principle framework diagram of the co-optimization algorithm of the present invention.
FIG. 5 is a schematic diagram of the operation of the ripple diffusion algorithm of the present invention.
Detailed Description
The present invention will be further described with reference to the following specific examples and drawings, but the embodiments of the present invention are not limited thereto.
As shown in fig. 1, the emergency rescue path planning method based on the traffic environment change of the urban road network provided by the embodiment includes the following steps:
1) and acquiring the traffic environment change information and emergency rescue information of the urban road network.
The urban road network traffic environment change information comprises urban road network intersection node information, urban road network section length information, historical data and real-time data of urban road network traffic flow, average vehicle running speed of each road section of the urban road network, historical information and real-time information of urban road network intersection node average traffic delay, and prediction information of the average vehicle running speed of each road section of the urban road network and the average traffic delay of road network intersection nodes; the emergency rescue information comprises the geographical position of a rescue starting point and the geographical position of a rescue ending point.
The urban road network intersection node information is as follows: the detailed road network structure is shown in fig. 2, assuming that the road network node scale is 400, 900, 1600, 2500, and each node in the road network is connected to at most 4 nodes.
The real-time data of the urban road network traffic flow is as follows: assuming an initial average speed v of each road section of a road network0Obey normal distribution of N to (40,5) and v is more than or equal to 00Less than or equal to 60, and the unit is km/h; average speed variation of road section (i, j)
Subject to uniform distribution, then
k is not less than 0, wherein vt+k|t(i, j) represents the average speed of the link (i, j) at the predicted time t + k, vt+k+1|t(i, j) represents the average speed of the link (i, j) at the predicted time t + k +1, and
the unit is km/h; average delay d of road network intersection nodesiAlso subject to uniform distribution, i.e. diU (20,60) in units of s.
The emergency rescue information is as follows: and assuming that the vertex at the lower left corner of the road network is a rescue starting point and the vertex at the upper right corner of the road network is a rescue terminal point.
2) According to the change information of the urban road network traffic environment, urban emergency rescue characteristics are analyzed, and emergency rescue path planning evaluation indexes are determined.
Analyzing urban emergency rescue characteristics: urban emergency rescue is a key means for guaranteeing the life safety of injured people, so the main goal of emergency rescue is to shorten the rescue time as much as possible and improve the emergency rescue efficiency, the level of the emergency rescue efficiency is mainly limited by the rescue path travel time, and the reliability of the path travel time is the key for ensuring that a rescue vehicle can arrive on time.
Selecting evaluation indexes: according to the urban emergency rescue characteristic analysis, determining emergency rescue path planning evaluation indexes including path travel time and reliability thereof, introducing path length as a general index in path planning, wherein T is used for representing the path travel time, R is used for representing the path length, and T is used for representing the path travel time85％And R85％Reflecting reliability, wherein T85％An 85 quantile representing the path travel time, i.e. 85% of the path travel time is less than this value; r85％An 85 quantile representing the path length, i.e. 85% of the path length is less than this value.
3) And constructing a basic framework for collaborative optimization of emergency rescue path planning according to the urban road network traffic environment change information, emergency rescue information, urban emergency rescue characteristics and emergency rescue path planning evaluation indexes.
The basic framework of the collaborative optimization is that the current position is used as a new starting point, the future urban road network traffic flow state is predicted through the historical data of the urban road network traffic flow, the urban road network traffic environment change information is obtained, and the path is planned on the basis, namely the planning process of the pathThe method is carried out in coordination with the change of the urban road network traffic environment; meanwhile, a fixed time step length T is setunitThe method is used for updating the urban road network traffic flow data and the urban road network traffic flow prediction information at regular time, and cooperatively optimizing the optimal path again according to the current position of the vehicle until the vehicle reaches a rescue terminal, in other words, the complete planning of the path is integrated by planning a plurality of sub-paths.
The collaborative optimization is a main objective of path travel time minimization:
in the formula (I), the compound is shown in the specification,
an optimal path, namely a sub-path, output at the moment t; p represents a set of sub-paths, and N (P) represents the number of sub-paths;
representing a path
A time cost function of; t represents the path travel time of the vehicle traveling along the sub-path set P; the objective function satisfies the following constraints:
I(P(i),P(i+1))＝1,i＝1,...,N(P)-1 (2)
wherein, I (P (I), P (I +1)) represents the connection state of the subpaths P (I) and P (I +1), wherein 1 represents connection, namely the starting point of P (I +1) is the end point of P (I), and 0 represents no connection;
representing a path
The number of included nodes;
representing a path
The ith node of (1);
indicating the node at the predicted time t + k
And node
The communication state between the two, wherein 1 represents communication, and 0 represents non-communication;
indicating the vehicle passing path at the predicted time t + k
Average delay of ith node;
indicating the vehicle passing path at the predicted time t + k
The travel time of a road section between the ith node and the (i +1) th node;
representing a path
Travel time cost function of.
The expressions (2) to (4) clearly indicate that the vehicle follows the path
The connectivity and the travel time of the sub-path are cooperatively changed along with time in the driving process, and the urban road network communication state and the urban road network traffic environment information of the sub-path at the predicted time t + k are obtained by dynamically evolving and predicting the urban road network traffic environment at the time t.
In this embodiment, a basic framework of the emergency rescue path planning collaborative optimization is constructed as shown in fig. 3, and first, the number of node selection steps is determined; then, taking the current node as a new starting point, predicting the future road network traffic flow state through the road network historical traffic flow data, and planning a path on the basis, namely, the path planning process and the road network traffic environment change are carried out in a coordinated manner; meanwhile, a fixed time step length T is setunitThe method is used for updating the road network traffic flow data and the prediction information and re-collaboratively optimizing the optimal path according to the current position of the vehicle until the vehicle reaches the rescue terminal, in other words, the complete planning of the path is integrated by planning a plurality of sub-paths. Wherein, let TunitAnd 1min, which means that the road network state is updated every 1 min.
The step of determining the node selection steps is as follows: setting the selection range of the selection steps to be 1-5; during calculation, a dynamic Dijkstra method is introduced to calculate a G value, an H value is calculated by using a straight-line distance between two nodes and a road network average speedometer at the current moment, and the calculation formula is as follows:
wherein L (i, j) is the linear distance between the node i and the node j, vaveThe calculation results of the average speed of the road network at the current time and different selected steps are shown in the following table 1:
TABLE 1 travel time in different selected steps
It can be seen that when the number of selection steps of the node is 5, the travel time is the shortest, and therefore, it is determined that the number of selection steps of the node is 5.
4) According to a basic framework of emergency rescue path planning collaborative optimization, a collaborative optimization algorithm is provided, and the core idea of the collaborative optimization algorithm comprises the following four parts:
the numerical attribute of the nodes: defining an F value attribute for each urban road network intersection node, wherein the calculation formula of the F value is as follows:
F(i)＝G(i)+H(i) (6)
wherein F (i) represents the F value of the node i; f value represents the movement cost of the current node to reach the end point of the road network through the node i; g (i) represents the G value of node i; h (i) represents the H value of node i; the value G represents the cost of movement from the current node to node i, and the value H represents the estimated cost from node i to the end of the road network.
Selecting a node: defining l as the selection step number of the node, and when l is 1, selecting the node as the adjacent node of the current node; when l is more than or equal to 2, the selection steps are as follows:
a. defining one attribute of a node as 'whether the node is marked', if the node is selected, the attribute of the node is marked, otherwise, the node is not marked; meanwhile, defining a set M to represent a set of selected nodes, and putting the selected nodes when l is 1 into the set M;
b. for all nodes in the set M, selecting adjacent nodes of each node, taking all adjacent nodes with the attribute of 'unmarked' as a new set M, namely updating the nodes in the set M to be newly selected unmarked adjacent nodes, and simultaneously increasing the value of l by 1;
c. if the value of l reaches the set value, namely the set selection step number is reached, the node in the set M is the final selected node, otherwise, the step b is returned, and the current node cannot be selected; if the end point is marked in the selection process, the end point is put into the set M, and all the following steps are skipped, namely only the end point in the set M.
And thirdly, combining the selection mode of the nodes mentioned in the second step, and calculating the G value by adopting a dynamic Dijkstra algorithm, wherein the core idea of the dynamic Dijkstra is as follows: calculating the path weight according to the prediction information of the urban road network traffic flow and the change of the urban road network traffic environment, namely the calculation process of the path weight and the change of the urban road network traffic environment are performed in a coordinated manner, firstly, an array Dis is stated to be used for storing the shortest travel time from a starting point to other nodes, and a set Q is used for storing the node with the shortest travel time; initially, only a starting point is in the set Q, the starting point is used as a current node, changes of urban road network traffic environment in a plurality of time units in the future are predicted according to the time of the current node, the predicted travel time between the current node and adjacent nodes of the current node and the predicted time of reaching each adjacent node are obtained, and the value of the corresponding node in the Dis is updated; then finding the minimum value and the corresponding node in the Dis, adding the minimum value and the corresponding node into the set Q, removing the minimum value and the corresponding node from the Dis, taking the node as the current node, and repeating the above process until all the nodes in the set M are added into the set Q, so that the predicted travel time, namely the value G, of all the selected nodes in the set M can be obtained; wherein the set M represents a set of selected nodes.
Fourthly, estimating the H value by adopting a ripple diffusion algorithm, wherein the ripple diffusion algorithm has the following principle: assume a road network intersection node has four attributes: inactive, waiting, activated and dead; propagating initial ripples from a rescue starting point, wherein the ripples are diffused towards adjacent unactivated nodes along a road section, the diffusion speed is consistent with the predicted traffic flow speed of the road section in the current time unit, namely the diffusion process of the ripples is performed in coordination with the change of the urban road network traffic environment; when the ripple reaches an inactivated node, the node state is converted into a waiting state, and the waiting time is consistent with the average delay of the node; after the waiting time is over, activating the node to generate new ripples, wherein the node is activated at the moment; when all the adjacent nodes of the node are activated, the node is converted into a death state; the first ripple to reach the rescue terminal determines the path travel time.
5) The emergency rescue path planning result is solved and output by using a collaborative optimization algorithm, as shown in fig. 4, the emergency rescue path planning method comprises the following steps:
5.1) setting the rescue starting point as a current node;
5.2) obtaining a selected node according to the current node, the node selection step number and the node selection step;
5.3) calculating the G value and the H value of the selected node by respectively applying a dynamic Dijkstra algorithm and a ripple diffusion algorithm, and obtaining the F value of the node by using a calculation formula of the F value; wherein, the flow of the ripple diffusion algorithm is shown in FIG. 5;
5.4) finding the minimum F value and the corresponding node thereof, wherein the node is the optimal next node, and the path between the starting point and the node is obtained and is the first sub-path;
5.5) the vehicle runs along the sub-path, and according to the actual road network traffic environment change condition, the travel time of the sub-path is obtained after the vehicle reaches the end point of the sub-path;
5.6) the end point of the sub-path is used as a new starting point, and on the basis of the actual time of reaching the node, the F value is calculated according to the selected node, the optimal next node is calculated, and the next sub-path is obtained.
5.7) repeating the step 5.5) until the rescue terminal is reached, wherein the set of all sub-paths is the final path.
And respectively outputting 10 times of path planning results of the collaborative optimization algorithm and the online optimization method on the urban road network with the number of N400, N900, N1600 and N2500. The online optimization method uses an A-algorithm and a Dijkstra algorithm based on online optimization, and the principle is as follows: calculating and determining the position of the next node from the starting point, namely determining the driving direction; and after the vehicle reaches the next node, repeatedly calculating and determining the next node based on the current position of the vehicle and the road network traffic environment at the current moment, so as to correct the driving direction of the vehicle until the vehicle reaches the terminal. The output emergency rescue path planning result is shown in table 2 below.
TABLE 2 mean travel time and Path Length values under different road networks
As can be seen from Table 2, compared with the online optimization method, the travel time of the collaborative optimization algorithm is shortened by 6.47% -45.95%; on the path length, the collaborative optimization algorithm is shortened by 1.16% -38.66% compared with the online optimization method.
TABLE 3 travel time and Path Length 85 quantiles under different road networks
Table 3 shows the travel time 85 quantile T of the collaborative optimization algorithm and the online optimization method under different road networks85％And 85 quantile of path length R85％. It can be seen that, at the travel time, the T of the algorithm is cooperatively optimized85％Smaller than that of an online optimization method; on the path length, when the road network size is N equal to 400, R of the collaborative optimization algorithm85％R than Dijkstra algorithm85％For a longer time, the shorter travel time does not necessarily mean a shorter path length, reflecting the effectiveness of the collaborative optimization algorithm in shortening the travel time in emergency rescue path planning. The experimental result table shows that the emergency rescue path planning method based on the path traffic environment change can output an emergency rescue path with shorter travel time and higher reliability, has important application value and is worthy of popularization.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that the changes in the shape and principle of the present invention should be covered within the protection scope of the present invention.
Claims (6)
1. An emergency rescue path planning method based on urban road network traffic environment change is characterized by comprising the following steps:
1) acquiring urban road network traffic environment change information and emergency rescue information;
2) according to the change information of the urban road network traffic environment, analyzing urban emergency rescue characteristics and determining emergency rescue path planning evaluation indexes;
3) constructing a basic framework for emergency rescue path planning collaborative optimization according to the urban road network traffic environment change information, emergency rescue information, urban emergency rescue characteristics and emergency rescue path planning evaluation indexes;
4) planning a basic framework of collaborative optimization according to the emergency rescue path, and providing a collaborative optimization algorithm;
5) and solving and outputting an emergency rescue path planning result by using a collaborative optimization algorithm.
2. The emergency rescue path planning method based on the urban road network traffic environment change as claimed in claim 1, wherein: in step 1), the urban road network traffic environment change information comprises urban road network intersection node information, urban road network section length information, historical data and real-time data of urban road network traffic flow, average running speed of vehicles of each road network, historical information and real-time information of urban road network intersection node average traffic delay, and prediction information of average running speed of vehicles of each road network and average traffic delay of road network intersection nodes; the emergency rescue information comprises the geographical position of a rescue starting point and the geographical position of a rescue ending point.
3. The emergency rescue path planning method based on the urban road network traffic environment change as claimed in claim 1, wherein the step 2) comprises the following steps:
2.1) analyzing the urban emergency rescue characteristics: urban emergency rescue is a key means for guaranteeing the life safety of injured people, so the emergency rescue aims to shorten rescue time as much as possible and improve emergency rescue efficiency, the emergency rescue efficiency is limited by rescue path travel time, and the reliability of the path travel time is a key for ensuring that rescue vehicles can arrive on time;
2.2) selecting evaluation indexes: according to the urban emergency rescue characteristic analysis, determining emergency rescue path planning evaluation indexes including path travel time and reliability thereof, introducing path length as a general index in path planning, wherein T is used for representing the path travel time, R is used for representing the path length, and T is used for representing the path travel time85％And R85％Reflecting reliability, wherein T85％An 85 quantile representing the path travel time, i.e. 85% of the path travel time is less than this value; r85％An 85 quantile representing the path length, i.e. 85% of the path length is less than this value.
4. The emergency rescue path planning method based on the urban road network traffic environment change as claimed in claim 1, wherein: in the step 3), a basic framework of collaborative optimization is to predict the future urban road network traffic flow state by using the current position as a new starting point through historical data of the urban road network traffic flow to obtain urban road network traffic environment change information, and then a path is planned on the basis, namely the planning process of the path is cooperatively performed with the urban road network traffic environment change; meanwhile, a fixed time step length T is setunitThe system is used for updating urban road network traffic flow data and urban road network traffic flow prediction information at regular time, and cooperatively optimizing the optimal path again according to the current position of the vehicle until the vehicle reaches a rescue terminal, in other words, the complete planning of the path is integrated by planning a plurality of sub-paths;
the collaborative optimization is a main objective of path travel time minimization:
in the formula (I), the compound is shown in the specification,
for the optimal path output at time t,i.e., a sub-path; p represents a set of sub-paths, and N (P) represents the number of sub-paths;
representing a path
A time cost function of; t represents the path travel time of the vehicle traveling along the sub-path set P; the objective function satisfies the following constraints:
I(P(i),P(i+1))＝1,i＝1,...,N(P)-1 (2)
wherein, I (P (I), P (I +1)) represents the connection state of the subpaths P (I) and P (I +1), wherein 1 represents connection, namely the starting point of P (I +1) is the end point of P (I), and 0 represents no connection;
representing a path
The number of included nodes;
representing a path
The ith node of (1);
indicating the node at the predicted time t + k
And node
The communication state between the two, wherein 1 represents communication, and 0 represents non-communication;
indicating the vehicle passing path at the predicted time t + k
Average delay of ith node;
indicating the vehicle passing path at the predicted time t + k
The travel time of a road section between the ith node and the (i +1) th node;
representing a path
A travel time cost function of;
the expressions (2) to (4) clearly indicate that the vehicle follows the path
The connectivity and the travel time of the sub-path are cooperatively changed along with time in the driving process, and the urban road network communication state and the urban road network traffic environment information of the sub-path at the predicted time t + k are obtained by dynamically evolving and predicting the urban road network traffic environment at the time t.
5. The emergency rescue path planning method based on the urban road network traffic environment change according to claim 1, characterized in that in step 4), the core idea of the collaborative optimization algorithm includes the following four parts:
the numerical attribute of the nodes: defining an F value attribute for each urban road network intersection node, wherein the calculation formula of the F value is as follows:
F(i)＝G(i)+H(i) (6)
in the formula, F (i) represents the value F of the node i, and the value F represents the movement cost of the current node to the end point of the road network via the node i; g (i) represents the G value of node i; h (i) represents the H value of node i; the G value represents the moving cost from the current node to the node i, and the H value represents the estimated cost from the node i to the road network terminal point;
selecting a node: defining l as the selection step number of the node, and when l is 1, selecting the node as the adjacent node of the current node; when l is more than or equal to 2, the selection steps are as follows:
a. defining one attribute of a node as 'whether the node is marked', if the node is selected, the attribute of the node is marked, otherwise, the node is not marked; meanwhile, defining a set M to represent a set of selected nodes, and putting the selected nodes when l is 1 into the set M;
b. for all nodes in the set M, selecting adjacent nodes of each node, taking all adjacent nodes with the attribute of 'unmarked' as a new set M, namely updating the nodes in the set M to be newly selected unmarked adjacent nodes, and simultaneously increasing the value of l by 1;
c. if the value of l reaches the set value, namely the set selection step number is reached, the node in the set M is the final selected node, otherwise, the step b is returned, and the current node cannot be selected; if the end point is marked in the selecting process, the end point is put into the set M, and all the following steps are skipped, namely only the end point exists in the set M;
and thirdly, combining the selection mode of the nodes mentioned in the second step, and calculating the G value by adopting a dynamic Dijkstra algorithm, wherein the core idea of the dynamic Dijkstra is as follows: calculating the path weight according to the prediction information of the urban road network traffic flow and the change of the urban road network traffic environment, namely the calculation process of the path weight and the change of the urban road network traffic environment are performed in a coordinated manner, firstly, an array Dis is stated to be used for storing the shortest travel time from a starting point to other nodes, and a set Q is used for storing the node with the shortest travel time; initially, only a starting point is in the set Q, the starting point is used as a current node, changes of urban road network traffic environment in a plurality of time units in the future are predicted according to the time of the current node, the predicted travel time between the current node and adjacent nodes of the current node and the predicted time of reaching each adjacent node are obtained, and the value of the corresponding node in the Dis is updated; then finding the minimum value and the corresponding node in the Dis, adding the minimum value and the corresponding node into the set Q, removing the minimum value and the corresponding node from the Dis, taking the node as the current node, and repeating the above process until all the nodes in the set M are added into the set Q, so that the predicted travel time, namely the value G, of all the selected nodes in the set M can be obtained; wherein the set M represents a set of selected nodes;
fourthly, estimating the H value by adopting a ripple diffusion algorithm, wherein the ripple diffusion algorithm has the following principle: assume a road network intersection node has four attributes: inactive, waiting, activated and dead; propagating initial ripples from a rescue starting point, wherein the ripples are diffused towards adjacent unactivated nodes along a road section, the diffusion speed is consistent with the predicted traffic flow speed of the road section in the current time unit, namely the diffusion process of the ripples is performed in coordination with the change of the urban road network traffic environment; when the ripple reaches an inactivated node, the node state is converted into a waiting state, and the waiting time is consistent with the average delay of the node; after the waiting time is over, activating the node to generate new ripples, wherein the node is activated at the moment; when all the adjacent nodes of the node are activated, the node is converted into a death state; the first ripple to reach the rescue terminal determines the path travel time.
6. The emergency rescue path planning method based on the urban road network traffic environment change according to claim 1, characterized in that in step 5), the operation steps using the collaborative optimization algorithm are as follows:
5.1) calculating the optimal next node from the rescue starting point by using a collaborative optimization algorithm according to the F value of the node to obtain a path between the starting point and the node, namely a first sub-path;
5.2) the vehicle runs along the sub-path, and according to the actual road network traffic environment change condition, the travel time of the sub-path is obtained after the vehicle reaches the end point of the sub-path;
5.3) the end point of the sub-path is used as a new starting point, and on the basis of the actual time of reaching the node, the optimal next node is calculated by applying the collaborative optimization algorithm again to obtain the next sub-path;
5.4) repeating the step 5.2) until the rescue terminal is reached.
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