CN113159379A - Public transport travel path planning method avoiding epidemic situation risk area - Google Patents
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Abstract
The invention discloses a public transport travel path planning method for avoiding epidemic situation risk areas, which comprises an information push module, a geographic area division module, an epidemic situation risk evaluation module, an epidemic situation risk early warning module, an epidemic situation period public travel dynamic path planning module and a traffic state monitoring module. The invention can receive the epidemic situation information in real time to evaluate the epidemic situation risk, dynamically sense the position information of the user, push the epidemic situation risk early warning information for the bus, plan a travel path for the vehicle to avoid the epidemic situation risk area and provide a feasible scheme for the travel in the epidemic situation period under the condition that the city is divided into a grid area, so that the travel of the user in the epidemic situation is safer and more convenient.
Description
Technical Field
The invention relates to the fields of mobile communication, automatic navigation and intelligent algorithms, in particular to a travel path planning method for avoiding epidemic situation risk areas.
Background
The influence of epidemic situation and the requirement of national epidemic prevention are met, the daily trip of residents is limited to a certain extent, and on the premise of guaranteeing personal safety of the residents, the residents can avoid the high-risk area of the epidemic situation, so that normal work, school and shopping can be realized, and meanwhile, the trip time is saved to become the focus of attention of the residents. The information of the epidemic situation risk area is not added in the current path selection algorithm, and residents need to delete the path by themselves, so that the travel time is increased, and inconvenience is brought.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a travel path planning method for avoiding epidemic situation risk areas.
The invention discloses a travel path planning method for avoiding epidemic situation risk areas, which comprises an information push module, a geographic area division module, an epidemic situation risk evaluation module, an epidemic situation risk early warning module, a public travel dynamic path planning module during an epidemic situation and a traffic state monitoring module, and the method comprises the following specific implementation steps:
and the S1 information pushing module is used for receiving epidemic situation information issued by the Weijian committee and pushing the epidemic situation information to the geographical region dividing module.
S2, a geographic area division module, which is used for meshing the community according to a four-level management system of district-street-community-grid, dividing the city into a plurality of grid areas and constructing a 2D continuous coordinate grid map; receiving epidemic situation information sent by the information pushing module, and matching the epidemic situation information with the geographic area;
the S3 risk assessment module receives the geographic information matched with the epidemic situation information, calculates the risk value of each region, and sends the region risk information to the risk early warning module and the public trip dynamic path planning system in the epidemic situation period;
the S4 risk early warning module receives the regional risk information, sends the risk early warning information to the bus and issues epidemic risk early warning information to passengers;
s5 public trip dynamic path planning module in epidemic situation period, receiving epidemic situation risk information, taking the risk minimum value as a path induction target, taking the risk value of nodes between networks as algorithm parameters in the path planning algorithm, selecting a path capable of avoiding the epidemic situation risk area for the user on the grid map, and sending the path information to the public transportation monitoring platform;
and S6, the public transportation monitoring platform dynamically senses the position information and the terminal information of the vehicle in real time.
Preferably, in step S2, the geographic area division module performs meshing on the community according to a four-level management system of "area-street-community-grid", divides the city into a plurality of grid areas, constructs a 2D continuous coordinate grid map, and matches epidemic situation information with geographic information.
Preferably, in the risk assessment module of step S3, the magnitude of the risk value of the area is determined by the interaction strength between the area and the risk value of the risk area, and the interaction strength is described by using a gravity model:
wherein V (i, j, t) represents the strength of association between the region i at time t and the risk region j; pi,PjThe standing population of the area i and the risk area j respectively; d (x)i,yi,xj,yj) The Euclidean distance between the area i and the risk area j;
population flow index for time t zone i and risk zone j:
wherein N (i, j, t) represents the sum of the visiting times of residents in the risk area j in the area i and the times of the residents in the area i arriving at the risk area j; max (N (i, j, t)) represents the maximum value of N (i, j, t) in each region of the city.
The correlation strength V (i, j, t) is normalized:
preferably, in the risk assessment module in step S3, the data source for calculating the population mobility index between the area i and the area j is urban resident travel OD data (single-day data), which includes five data contents, namely, longitude of a starting point, latitude of a starting point, longitude of an ending point, latitude of an ending point, and OD intensity (number of people), and the spatial scale is a community scale; after spatial calibration, the OD data is spatially correlated on the scale of the grid area divided by the geographic area dividing module of S2.
Preferably, in the risk assessment module of step S3, the risk value of the area i at time t is:
wherein R isiThe risk value for region i; a. thed,jThe cumulative number of confirmed cases for risk area j; a. then,jThe number of newly added cases on the day of risk zone j.
Preferably, when the path planning module in step S5 selects a path, an improved a-star algorithm combined with a node risk value is adopted, and a node risk value is introduced into an heuristic function of the a-star algorithm, so that the node risk affects the path selection and avoids a high risk area.
The formula for improving the A-star algorithm is as follows:
wherein f (n) is an evaluation function; g (n) is the distance from the starting point to the node; l (n) is a heuristic function; h (n) is the Manhattan distance from the current node to the terminal point; v (n) is the risk value of the nth node.
The invention can receive the epidemic situation information in real time to evaluate the epidemic situation risk, dynamically sense the position information of the user, push the epidemic situation risk early warning information for the bus, plan a travel path for the vehicle to avoid the epidemic situation risk area and provide a feasible scheme for the travel in the epidemic situation period under the condition that the city is divided into a grid area, so that the travel of the user in the epidemic situation is safer and more convenient.
Drawings
Fig. 1 is a structural diagram of a public transportation travel path planning method for avoiding epidemic situation risk areas in the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
A public transport travel path planning method avoiding epidemic situation risk areas comprises an information push module, a geographical area division module, an epidemic situation risk evaluation module, an epidemic situation risk early warning module, a public travel dynamic path planning module during an epidemic situation and a traffic state monitoring module, and the method is specifically realized by the following steps:
and the S1 information pushing module is used for receiving epidemic situation information issued by the Weijian committee and pushing the epidemic situation information to the geographical region dividing module.
S2, a geographic area division module, which is used for meshing the community according to a four-level management system of district-street-community-grid, dividing the city into a plurality of grid areas and constructing a 2D continuous coordinate grid map; receiving epidemic situation information sent by the information pushing module, and matching the epidemic situation information with the geographic area;
the risk assessment module is used for receiving the geographic information matched with the epidemic situation information, calculating the risk value of each region, and sending the region risk information to the risk early warning module and the public trip dynamic path planning method during the epidemic situation;
the S4 risk early warning module receives the regional risk information, sends the risk early warning information to the bus and issues epidemic risk early warning information to passengers;
s5 public trip dynamic path planning module in epidemic situation period, receiving epidemic situation risk information, taking the risk minimum value as a path induction target, taking the risk value of nodes between networks as algorithm parameters in the path planning algorithm, selecting a path capable of avoiding the epidemic situation risk area for the user on the grid map, and sending the path information to the public transportation monitoring platform;
and S6, the public transportation monitoring platform dynamically senses the position information and the terminal information of the vehicle in real time.
In the invention, the geographic area division module in the step S2 meshes the community according to a four-level management system of district-street-community-grid, divides the city into a plurality of grid areas, constructs a 2D continuous coordinate grid map, and matches epidemic situation information with geographic information.
In the present invention, in the risk assessment module in step S3, the magnitude of the risk value of the area is determined by the interaction strength between the area and the risk value of the risk area, and the interaction strength is described by using a gravity model:
wherein V (i, j, t) represents the strength of association between the region i at time t and the risk region j; pi,PjThe standing population of the area i and the risk area j respectively; d (x)i,yi,xj,yj) The Euclidean distance between the area i and the risk area j;
population flow index for time t zone i and risk zone j:
wherein N (i, j, t) represents the sum of the visiting times of residents in the risk area j in the area i and the times of the residents in the area i arriving at the risk area j; max (N (i, j, t)) represents the maximum value of N (i, j, t) in each region of the city.
The correlation strength V (i, j, t) is normalized:
in the present invention, in the risk assessment module in step S3, the data source for calculating the population mobility index between the area i and the area j is the urban resident travel OD data (single day data), which includes five data contents, that is, the longitude of the start point, the latitude of the start point, the longitude of the end point, the latitude of the end point, and the OD intensity (number of people), and the spatial scale is the community scale; after spatial calibration, the OD data is spatially correlated on the scale of the grid area divided by the geographic area dividing module of S2.
In the present invention, in the risk assessment module in step S3, the risk value of the area i at time t:
wherein R isiThe risk value for region i; a. thed,jThe cumulative number of confirmed cases for risk area j; a. then,jThe number of newly added cases on the day of risk zone j.
In the invention, when the path planning module in the step S5 selects a path, an improved a-star algorithm combined with a node risk value is adopted, and a node risk value is introduced into an heuristic function of the a-star algorithm, so that the node risk affects the path selection and avoids a high risk area.
The formula for improving the A-star algorithm is as follows:
wherein f (n) is an evaluation function; g (n) is the distance from the starting point to the node; l (n) is a heuristic function; h (n) is the Manhattan distance from the current node to the terminal point; v (n) is the risk value of the nth node.
The invention comprises the following steps: the information pushing module receives epidemic situation information issued by the Weijian committee and pushes the epidemic situation information to the geographic region dividing module; the geographic area division module is used for meshing the communities according to a four-level management system of district-street-community-grid, dividing the city into a plurality of grid areas and constructing a 2D continuous coordinate grid map; receiving epidemic situation information sent by the information pushing module, and matching the epidemic situation information with the geographic area; the risk evaluation module receives the geographic information matched with the epidemic situation information, calculates the risk value of each region, and sends the region risk information to the risk early warning module and the public trip dynamic path planning system during the epidemic situation; after receiving the regional risk information, the risk early warning module sends the risk early warning information to the bus and issues epidemic risk early warning information to passengers; the public trip dynamic path planning module receives epidemic situation risk information, takes a risk minimum value as a path induction target, takes a risk value of a node between networks as an algorithm parameter in a path planning algorithm, selects a path capable of avoiding an epidemic situation risk area for a user on a grid map, and sends the path information to a public transportation monitoring platform; and the public traffic monitoring platform dynamically senses the position information and the destination information of the vehicle in real time.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (6)
1. A public transport travel path planning method avoiding epidemic situation risk areas is characterized by comprising an information pushing module, a geographic area dividing module, an epidemic situation risk evaluation module, an epidemic situation risk early warning module, a public travel dynamic path planning module during an epidemic situation and a traffic state monitoring module, and the method is specifically implemented by the following steps:
the S1 information pushing module is used for receiving epidemic situation information issued by Weijian committee and pushing the epidemic situation information to the geographical region dividing module;
s2, a geographic area division module, which is used for meshing the community according to a four-level management system of district-street-community-grid, dividing the city into a plurality of grid areas and constructing a 2D continuous coordinate grid map; receiving epidemic situation information sent by the information pushing module, and matching the epidemic situation information with the geographic area;
the S3 risk assessment module receives the geographic information matched with the epidemic situation information, calculates the risk value of each region, and sends the region risk information to the risk early warning module and the public trip dynamic path planning system in the epidemic situation period;
the S4 risk early warning module receives the regional risk information, sends the risk early warning information to the bus and issues epidemic risk early warning information to passengers;
s5 public trip dynamic path planning module in epidemic situation period, receiving epidemic situation risk information, taking the risk minimum value as a path induction target, taking the risk value of nodes between networks as algorithm parameters in the path planning algorithm, selecting a path capable of avoiding the epidemic situation risk area for the user on the grid map, and sending the path information to the public transportation monitoring platform;
and S6, the public transportation monitoring platform dynamically senses the position information and the terminal information of the vehicle in real time.
2. The method for planning the public transportation travel path avoiding the epidemic situation risk areas according to claim 1, wherein the step S2 comprises the steps of meshing the community by the geographic area division module according to a four-level management system of district-street-community-grid, dividing the city into a plurality of grid areas, constructing a 2D continuous coordinate grid map, and matching the epidemic situation information with the geographic information.
3. The method for planning a public transportation travel path avoiding an epidemic situation risk area according to claim 1, wherein in the risk assessment module of step S3, the magnitude of the risk value of an area is determined by the interaction strength between the area and the risk value of the risk area, and the interaction strength is described by using a gravity model:
wherein V (i, j, t) represents the strength of interaction between region i and risk region j at time t; pi，PjThe standing population of the area i and the risk area j respectively; d (x)i，yi，xj，yj) The Euclidean distance between the area i and the risk area j;
population flow index for time t zone i and risk zone j:
wherein N (i, j, t) represents the sum of the visiting times of residents in the risk area j in the area i and the times of the residents in the area i arriving at the risk area j; max (N (i, j, t)) represents the maximum value of N (i, j, t) in each region of city;
the interaction intensities V (i, j, t) were normalized:
4. the method for planning the travel path of public transportation avoiding the epidemic situation risk areas according to claim 1, wherein in the risk assessment module of step S3, the data source for calculating the population mobility index between the area i and the area j is the travel OD data (single day data) of urban residents, including five data contents of the longitude of the starting point, the latitude of the starting point, the longitude of the ending point, the latitude of the ending point and the OD intensity (number of people), and the spatial scale is the scale of the community; after spatial calibration, the OD data is spatially correlated on the scale of the grid area divided by the geographic area dividing module of S2.
5. The method for planning a public transportation travel path avoiding an epidemic situation risk area according to claim 1, wherein in the risk assessment module of step S3, the risk value of area i at time t:
wherein R isiThe risk value for region i; a. thed，，tThe cumulative number of confirmed cases for risk area j; a. then，，tjThe number n is the number of cases of risk areas in the whole city.
6. A travel path planning method for avoiding an epidemic situation risk area according to claim 1, wherein the path planning module in step S5 adopts an improved a-star algorithm combined with node risk values when performing path selection, and introduces node risk values into an heuristic function of the a-star algorithm, so that node risk influences path selection to avoid high risk areas; the formula for improving the A-star algorithm is as follows:
wherein f (n) is an evaluation function; g (n) is the distance from the starting point to the node; j (n) is a heuristic function; h (n) is the Manhattan distance from the current node to the terminal point; v (n) is the risk value of the nth node.
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