CN116127786A - System and method for measuring and calculating security state of slow traffic group based on city slow traffic simulation - Google Patents
System and method for measuring and calculating security state of slow traffic group based on city slow traffic simulation Download PDFInfo
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Abstract
The invention discloses a system and a method for measuring and calculating security states of a slow-going group based on urban slow-going traffic simulation, which specifically comprise the following steps: parameterizing behavior habits and living habits of four slow-going groups to obtain an alternative path set of the four slow-going groups; mapping building outlines, cell boundaries and motor vehicle lanes drawn based on ArcGIS into an analog model one by one; constructing a walking and riding network, and mapping a slow route randomly matched with a slow intelligent agent into a navigation target set by using a undirected graph algorithm; and calculating the average moving speed, the space distribution density and the area ratio of the pedestrians occupying the motor vehicle lanes of the slow-going group. The invention constructs a physical space model of the movement of the multi-element main body, designs interactive logic of various slow-going main bodies and street environments, simulates the actual movement whole process of various main bodies, and provides technical support for simulating the adaptability of the slow-going main bodies in complex road environments.
Description
Technical Field
The invention relates to the technical field of traffic industry, in particular to an urban slow traffic simulation modeling and slow group crowding measuring and calculating system and method based on analog and ArcGIS.
Background
The traditional slow traffic safety evaluation method focuses on analyzing the physical and psychological influence of road environment characteristics on slow groups from one-sided and static view angles, but less quantifies the actual safety influence of the road environment on the slow groups in an actual or planning scheme, so that the direct connection between a safety evaluation result and a road environment improvement strategy is weaker.
How to construct a simulation model with the traveling habit of the slow-going main body and enable the simulation model to dynamically interact with an environment model is a technical means needed in the current slow-going traffic special planning and compiling work. Complex system simulation software represented by analog provides a pedestrian library considering social force effect, and can better simulate the adaptability of pedestrians in complex road environments, but the model software has the limitations of low modeling efficiency, space analysis capability and the like.
Disclosure of Invention
The invention aims to solve the technical problems of breaking through the difference of vector data structures among different analysis software, being compatible with the advantages of GIS (Geographic Information System ) analysis tools in terms of space modeling, space analysis, analog simulation and the like, providing an efficient and convenient rapid modeling method for multi-main road environment, evaluating the trip safety state of slow-going groups and verifying the validity of an updating strategy.
The invention adopts the following technical scheme to solve the technical problems:
the invention provides a method for measuring and calculating security states of slow-going groups based on urban slow-going traffic simulation, which comprises the following steps:
s1: parameterizing the social slow-going group travel habits, and generating a random route collection conforming to the identity of the intelligent agent; the method comprises the following specific steps:
step 11, inducing hundred-degree POI points into two types of position points and service points, setting various slow groups to enter from the position points, passing through a plurality of service points and finally leaving a working area from the position points;
step 12, refining action logics of four slow-going groups consisting of working groups, parents, students and residents not including the groups, and definitely conforming to the in-out position points of the identities and the categories and the number of POI points required to be experienced;
step 13, randomly selecting corresponding POI points from the local POI data set according to the number of various travel destinations to be reached by various walking groups in step 12 to form a target point set;
step 14, carrying out geographic registration on the regional topographic map in an ArcGIS, drawing a walking network along the center line of the sidewalk, and drawing a riding network along the boundary between the sidewalk and the motor vehicle lane to extend to the motor vehicle lane;
step 15, breaking the pedestrian network and the walking network according to break points, and converting the walking network and the riding network into an undirected graph model;
step 16, further obtaining the shortest walking path between the adjacent target points in the step 13 based on the undirected graph object of the walking network, recording the journey in the form of a coordinate set of the path end points, obtaining an alternative walking path set, and obtaining an alternative riding path set in the same way;
step 17, creating a target line in the analog to navigate the slow group based on the coordinate set of all path end points.
S2: constructing a road environment vector model, and directly mapping GIS vector elements into an analog simulation model to replace manual modeling; the method comprises the following steps:
step 21, drawing wall elements and a road network based on ArcGIS, marking the positions of a parking space and a bus station in the form of vector points, writing the names of the roads to which the wall elements belong and the space orientation information relative to the extending direction of the roads into an attribute table, converting various vector elements into a Geojson format, and adjusting the space positions of the elements to be near (0, 0) by using Python codes;
step 22, automatically converting the wall elements in the Geojson format into analog wall elements by using the Python codes: adding auxiliary points for representing the front-back trend on the basis of the folding points of the line segments of the wall elements; writing the position information of the starting point, the folding point and the auxiliary point of each line element relative to the starting point into the corresponding position of the wall element in the analog in an XML format;
step 23, mapping road network, road section and parking space elements in a Geojson format into corresponding element objects in the analog by using a Python code: constructing a road network object at a corresponding position in an analog by using an XML format, embedding road section and parking space elements in the road network object, and creating a unique element name and element ID for the road network and auxiliary objects thereof;
step 24: automatically generating a parking space occupation element and a occupation control plug-in by using a Python code, and writing the occupation control plug-in into a corresponding position of a target surface element in the analog in an XML format;
and 25, manually adding or adjusting intersection elements and stop line elements for the automatically generated road network, and adding traffic lights to each pedestrian crossing channel to bind the pedestrian crossing channel with the corresponding stop line.
S3, constructing a slow-going group and a motor vehicle movement logic; the method comprises the following steps:
step 31, setting slow-running main body action logic: the intelligent agent is generated from the edge of the sidewalk and the entrance and exit of the district, a target line sequence provided by a shortest path algorithm is used as a navigation medium, the intelligent agent is connected to each POI point in a preset route at the minimum distance cost, the intelligent agent stays on the corresponding POI point for a corresponding time, and finally the intelligent agent leaves the working area from the edge of the sidewalk or the entrance and exit of the district through all the POI points;
step 32, setting movement logic for motor vehicle groups: the motor vehicle is generated at the edge of a motor vehicle lane, the bus is set to stay or queue at a bus station, a part of non-bus motor vehicles stay at a selected parking space, and a part of non-bus motor vehicles directly pass through and leave a working area from the edge of the motor vehicle lane;
step 33, logic for setting pedestrian crossing and vehicle deceleration: when the pedestrians reach target lines on two sides of each pedestrian path, the vehicles reaching or approaching the parking lines are decelerated;
step 34, logic for setting the occupied space of the parking space to block pedestrian flow: when a vehicle enters a parking space, the occupied area corresponding to the parking space cannot enter, and after the motor vehicle leaves the parking space, the occupied area is opened to pedestrians again.
S4, simulating a block slow-going group activity scene from a non-school time period to a school time period, and evaluating congestion state change, wherein the specific steps are as follows:
step 41, constructing a Track event and a Record event to Record pedestrian space-time Track information;
step 42, writing the space-time track of the slow-line main body into the shape vector file in a time-division manner;
and 43, reading a shape file by using the ArcGIS, and calculating the occupied area of pedestrians on motor lanes at different times, the average population density on the sidewalk and the average moving speed of the slow-moving group.
As a further specific scheme of the present invention, in step 15, the walking network undirected graph is constructed by the following specific steps:
A. reading the starting point coordinates and Euclidean distance of each road section in the walking network one by using a Python script, and converting the starting point coordinates and Euclidean distance into a text form;
B. and reading the walking network in the text form by using a third party Python module network to construct the undirected graph object.
As a further specific scheme of the present invention, in step 15, the riding network undirected graph is constructed by the following specific steps:
A. reading starting point coordinates and Euclidean distances of all road sections in a riding network one by using a Python script, and converting the starting point coordinates and Euclidean distances into a text form;
B. and reading the riding network in the text form by using a third party Python module network to construct an undirected graph object.
The invention also provides a system for measuring and calculating the security state of the slow-going group based on the urban slow-going traffic simulation, which specifically comprises the following steps:
a random route generation module for: parameterizing social slow-going group travel habits and generating a random route collection;
the road environment vector model module is used for: constructing a road environment vector model, and directly mapping GIS vector elements into an analog simulation model for manual adjustment;
a mobile logic building module for: constructing a slow-going group and motor vehicle movement logic: acquiring alternative paths of the slow-going group by utilizing a shortest path algorithm based on the walking network, the riding network and POI distribution of interest points in the area;
the congestion degree calculating and evaluating module is used for calculating and evaluating the congestion degree of the slow traffic: writing the alternative paths and the navigation target lines into an analog simulation template, designing operation logic for the slow-driving groups and the motor vehicles, and calculating the average moving speed, the space distribution density and the occupied area ratio index of the motor vehicle occupied by pedestrians of various slow-driving groups.
The invention also proposes an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the invention as described above.
Finally, the invention proposes a computer-readable storage medium storing computer instructions for causing a computer to perform the method according to the invention.
Compared with the prior art, the invention has the technical effects that:
the invention constructs five kinds of intelligent bodies which accord with the traveling habits and the motor vehicle movement logic of four kinds of slow-going groups of working groups, residents (non-parents or students), parents and students, and establishes a pedestrian route collection based on actual working, learning and living environments.
The invention constructs the conversion algorithm from the GIS vector elements to the environment elements in the analog model, thereby greatly improving the modeling efficiency of urban road traffic and avoiding repeated drawing and modeling work of cell boundaries, roads, building outlines and other lines and surface vector elements.
3) The invention designs a technical method for presetting a path for an analog intelligent body, which maps pedestrian routes randomly matched with the intelligent body into a collection of navigation target lines, so that the analog intelligent body has the capability of personalized route selection and social force function, thereby improving the simulation effect on real people flow.
4) The invention provides an evaluation index and a calculation method of the congestion degree of a slow traffic group, which can observe the index changes of the average moving speed, the space distribution density and the occupied area ratio of pedestrians to motor vehicle lanes in each period and each area.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings used for describing the embodiments will be briefly described below.
FIG. 1 is an overall flow chart of a road space modeling method.
Fig. 2 is a diagram of four types of slow-going population travel sequences.
FIG. 3 is a sequence diagram of slow-going subject action logic.
FIG. 4 is a sequence diagram of motor vehicle behavior logic.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings in conjunction with examples.
The invention provides a method for rapidly modeling urban road space based on static data such as vector road network, POI points, slow-going group travel habit parameters and the like, which utilizes indexes such as average moving speed, space distribution density, occupied area of motor vehicles and the like to dynamically evaluate the effectiveness of slow-going group travel safety and street updating strategies. The invention provides a rapid modeling method for urban road space and measurement and calculation of security indexes of slow-going groups. FIG. 1 is an overall flowchart of a road space modeling method, comprising the following specific steps:
step 1: parameterizing social slow-going population travel habits and generating a random route set.
Step 2: and constructing a road environment vector model, mapping the road environment vector model into an analog model and manually adjusting the road environment vector model.
Step 3: and building a working group, residents (non-parents or students), parents and students four slow-going groups and motor vehicle movement logic.
Step 4: simulating a block slow-going group activity scene from a non-school-release period to a school-release period, and calculating a slow-going traffic congestion index: average moving speed, space distribution density and area occupying ratio index of pedestrian occupying motor vehicle lane of various slow-going groups.
The method comprises the following specific steps:
and 11, summarizing hundred-degree POI points into location points (cell import and export, workgroup entrance and parent waiting area) and service points (restaurant catering, work and study, daily consumption, other consumption, leisure squares and student schools). Setting four slow-going groups to enter from the position points, pass through a plurality of service points and finally leave the working area from the position points.
Step 12: action logic to refine four types of slow-going populations: the work group enters the sheet area through the specific port, and leaves through the initial port or takes a bus after daily consumption, leisure and entertainment activities are carried out; the resident group enters the street from the entrance and exit of the residential district, and returns to the entrance and exit of the district after daily consumption, leisure and entertainment activities are carried out; parents enter the street from the district entrance and exit, reach waiting points near kindergarten, primary school and middle school in the district, stay for a plurality of times, and return to the district entrance and exit; during school, students enter the street from the gate of the school, part of the students go to the waiting point of parents and leave through the entrance and exit of the district after meeting the parents, and the other part of the students directly reach the entrance and exit of the district, as shown in fig. 2.
Step 13: and (3) randomly selecting corresponding POI points from the local POI data set according to the number of various travel destinations to be reached by various walking groups in the step (12) to form a target point set.
Step 14: carrying out geographic registration on the regional topographic map in the ArcGIS, converting the coordinate system into a CGS2000 projection coordinate system, drawing a walking network along the center line of the pavement, and drawing a riding network along the boundary of the pavement and the motor vehicle lane by extending one meter to the motor vehicle lane.
Step 15: and breaking the pedestrian network and the walking network according to break points. Reading starting point coordinates (X) of each line segment constituting the walking network using Python script o , Y o ) And end point coordinates (X) d , Y d ) And converting absolute values of the starting point and the ending point after the abscissa and the ordinate are rounded into character strings, splicing the character strings to serve as a starting point ID and an ending point ID, and taking Euclidean distance between the starting point and the ending point as a weighted value. And constructing an undirected graph object by using a third party Python module network, and then inputting the starting point ID, the end point ID and the distance weight of each component line segment of the walking network into the undirected graph one by one. Repeating the steps for the riding network.
Step 16: based on the undirected graph object of the walking network, the shortest walking path between the adjacent POI points in the step 13 is further obtained, and the route is recorded by the coordinate set of the path end points, so that an alternative walking route set is obtained. An alternative set of riding routes is then obtained in the same way.
Step 17: and summarizing and de-duplicating the endpoint coordinate sets of all paths, and creating target lines for navigating the slow group in the analog based on the endpoint coordinate sets, wherein each navigation target line is formed by rounding the abscissa and the ordinate respectively and then is spliced to be used as an ID and a name.
Step 21: vector elements such as cell boundaries, building outlines, motor vehicle lane center lines, pavement boundary lines and the like are drawn based on ArcGIS, positions of parking spaces and bus stops are marked in the form of vector points, and information such as road names, left and right sides relative to the extending direction of roads and the like to which the vector elements belong are written into an attribute table. The invention uniformly converts the drawn vector elements into a Geojson format text, reads coordinate information of points and line elements by using Python codes, records the minimum value (adjust_x, adjust_y) of the transverse and longitudinal coordinates of all point sets as correction values of all point sets, sets the transverse and longitudinal coordinates of all the points and line elements to respectively subtract the adjust_x and the adjust_y, and moves the whole working section to the vicinity of (0, 0).
Step 22: the wall elements of the Geojson format text are converted to analog wall elements using Python codes. The wall elements in the analog are required to be added with auxiliary points used for representing the front-back trend on the basis of line segment folding points, wherein the direction of the front auxiliary point is determined by the last folding point and the folding point together, the rear auxiliary point is determined by the folding point and the next folding point together, and the straight line distances from the upper auxiliary point and the lower auxiliary point to the folding point are all 25 pixel distances. And calculating the coordinates of each break point and the auxiliary point relative to the starting point by taking the 1 st break point in the point set as the starting point to form a new point set. And writing the starting point and the folding point set of each line element into the corresponding position of the wall element in the analog in an XML format, and generating a unique element name and element ID for the wall element.
Step 23: using Python codes to map elements such as road networks, road sections, parking spaces and the like of the Geojson format text into the analog: firstly, constructing basic road network and road section elements for an analog model by adopting the method in the step 22, then reading vector point information of roadside parking spaces and bus stations, constructing corresponding elements according to the direction of the point elements relative to the trend of the motor vehicle lanes and the linear distance of the point elements relative to the road start points, and writing the corresponding elements into the road section objects. In this step, basic information such as unified width of lanes, number of forward and reverse lanes and the like in the road network is set.
Step 24: and automatically obtaining the parking space occupation elements and the occupation control plug-in by using the Python codes. According to the invention, the occupation range of each parking space is simplified into small square target surface elements, and 5 boundary folding points (x-2, y-2), (x+2, y+2), (x-2, y+2) and (x-2, y-2) are automatically generated for each parking space according to the central point (x, y) of the parking space. And writing the 5 folding points into the corresponding positions of the target surface elements in the analog in an XML format, and generating the target surface names and IDs for the target surface elements. According to the target surface name, a "Ped Area Descriptor" control plug-in is added to the target surface to control whether a pedestrian can enter the target surface.
Step 25: the method comprises the steps of manually adding intersection elements for automatically generated road sections, connecting the independent road sections into a road network, adding stop lines on two sides of a pavement, controlling motor vehicles to slow down when pedestrians cross streets, and finally adding traffic lights for each pedestrian crossing channel to bind the corresponding stop lines.
Step 31: the slow-going body action logic is set and the flow is shown in figure 3. The pedestrian agent is generated by a pedSource module, one of alternative walking or riding path combination sets is randomly selected, target lines experienced by the pedestrian agent are sequentially added into a goTarget set, dwell time is added into the gotarge set according to the POI type corresponding to each target line, if the target line does not correspond to any POI point, the dwell time is set to be 0, the first entry mark in the goTarget is taken as the position where the pedestrian appears, and is marked as o_target, the last entry mark is taken as the position where the pedestrian leaves, and is marked as d_target. The pepdSource module is connected to the pepdEnter module, where pedestrians are set to originate from the o_target location. And then connecting the pedEnter module to the pedSelectOutput module, wherein the pedSelectOutput has two ports, the port 1 is connected to the pedWait module, the target line is taken out from the GoTarget in a non-replacement form (go target. Remove (0)) one by one as the stay position of the pedWait, the corresponding stay time is taken out from the GoArea in a non-replacement form (go area. Remove (0)) one by one as the stay time of the pedWait, the interrupt condition of the port 1 is set as "| ped. Go target. IsEmpty ()", the slow body reaches the target position according to the given target line sequence, and the corresponding stay time according to the POI type. The Port 2 is connected to the petGoto module, followed by the petSink module, setting the pedestrian to eventually arrive at d_target, and leave the workspace.
Step 32: movement logic is provided for the motor vehicle group as shown in fig. 4. The motor vehicle is generated by a car source module, which sets the motor vehicle to be generated from a certain lane of a certain road section. Making the carSource connect to the SelectOutput module, selectOutput has three ports: port 1 does not perform a specific operation nor does it stay; the port 2 sets the motor vehicle to pass through a certain parking space, and sets the stay time through a delay module; the port 3 sets the vehicle as a bus, the bus passes through a certain bus stop, and the delay module sets the stay time. All three ports are connected to the carMoveTo module and then to the carDispose module, and after setting that various motor vehicles finally arrive at the forward lane of a certain road section, leave the working area. In the case of no route being found, the motor vehicle is set to reach the reverse lane of a certain road section and then leave the working area.
Step 33: logic for pedestrian crossing and vehicle deceleration is set. In the petWait module in the slow group movement logic, when the pedestrian reaches the target lines on both sides of each pedestrian path, the traffic light is set to be switched to the red light stage, so that the vehicle reaching or approaching the stop line is controlled to be decelerated.
Step 34: logic for setting the occupied area of a parking space to block pedestrian flow is set. The invention sets that when the vehicle enters a parking space, ped Area Descriptor is utilized to control the occupied area corresponding to the parking space to be inaccessible, and the occupied area is opened to pedestrians again after the motor vehicle leaves the parking space.
Step 41: and constructing a Track event and a Record event to Record pedestrian space-time information. Firstly, in Track, the ID, abscissa and ordinate information of all pedestrian bodies are obtained by using a level. Getpes () function, and are written into data sets collection_id, collection_x and collection_y respectively, and meanwhile, event trigger time is also written into collection_time. And (3) reconstructing a Record event, and writing information in collection_id, collection_x, collection_y and collection_time into an Excel table 'trace. Xls' externally connected with the analog according to the sequence. And reading the slow-line main body space-time information in the table in real time by using the Python script, deleting the trace. Xls, and reconstructing the empty file, so that the size of the trace. Xls is ensured to be smaller than 5Mb all the time.
Step 42: and converting the space-time track of the slow-line main body into a CGS2000 projection coordinate system, and then writing the space-time information of the slow-line main body into a Shapefile vector file in time intervals by utilizing a third party Python module 'Shapefile'.
Step 43: reading a shapefile by using an ArcGIS, and calculating the occupied areas of pedestrians on the motor vehicle lanes at different times by using a space superposition analysis algorithm; calculating average population density on the sidewalk in different time periods by using a space statistical analysis algorithm; based on the ID information of the rider and the walker, the average moving speed of the walker in different road segment sections in each time segment is calculated. And saving the calculation result to the local in a table form.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. A slow-going group safety state measuring and calculating method based on city slow-going traffic simulation is characterized by comprising the following steps:
s1, parameterizing the travel habits of a social slow-going group and generating a random route collection conforming to the identity of an agent;
s2, constructing a road environment vector model, and directly mapping GIS vector elements into an analog simulation model to replace manual modeling;
s3, constructing a slow-going group and a motor vehicle movement logic;
s4, simulating a block slow-going group activity scene from the non-school time period to the school time period, and evaluating the congestion state change.
2. The method according to claim 1, wherein step S1 comprises the specific steps of:
step 11, inducing hundred-degree POI points into two types of position points and service points, setting various slow groups to enter from the position points, passing through a plurality of service points and finally leaving a working area from the position points;
step 12, refining action logics of four slow-going groups consisting of working groups, parents, students and residents not including the groups, and definitely conforming to the in-out position points of the identities and the categories and the number of POI points required to be experienced;
step 13, randomly selecting corresponding POI points from the local POI data set according to the number of various travel destinations to be reached by various walking groups in step 12 to form a target point set;
step 14, carrying out geographic registration on the regional topographic map in an ArcGIS, drawing a walking network along the center line of the sidewalk, and drawing a riding network along the boundary between the sidewalk and the motor vehicle lane to extend to the motor vehicle lane;
step 15, breaking the pedestrian network and the walking network according to break points, and converting the walking network and the riding network into an undirected graph model;
step 16, further obtaining the shortest walking path between the adjacent target points in the step 13 based on the undirected graph object of the walking network, recording the journey in the form of a coordinate set of the path end points, obtaining an alternative walking path set, and obtaining an alternative riding path set in the same way;
step 17, creating a target line in the analog to navigate the slow group based on the coordinate set of all path end points.
3. The method according to claim 2, characterized in that in step 15, the walking network undirected graph is constructed by the following specific steps:
A. reading the starting point coordinates and Euclidean distance of each road section in the walking network one by using a Python script, and converting the starting point coordinates and Euclidean distance into a text form;
B. and reading the walking network in the text form by using a third party Python module network to construct the undirected graph object.
4. The method according to claim 2, characterized in that in step 15, the riding network undirected graph is built by the following specific steps:
A. reading starting point coordinates and Euclidean distances of all road sections in a riding network one by using a Python script, and converting the starting point coordinates and Euclidean distances into a text form;
B. and reading the riding network in the text form by using a third party Python module network to construct an undirected graph object.
5. The method according to claim 1, characterized in that step S2 is specifically as follows:
step 21, drawing wall elements and a road network based on ArcGIS, marking the positions of a parking space and a bus station in the form of vector points, writing the names of the roads to which the wall elements belong and the space orientation information relative to the extending direction of the roads into an attribute table, converting various vector elements into a Geojson format, and adjusting the space positions of the elements to be near (0, 0) by using Python codes;
step 22, automatically converting the wall elements in the Geojson format into analog wall elements by using the Python codes: adding auxiliary points for representing the front-back trend on the basis of the folding points of the line segments of the wall elements; writing the position information of the starting point, the folding point and the auxiliary point of each line element relative to the starting point into the corresponding position of the analog middle wall element in an XML format; generating a unique element name and element ID for each section of wall element;
step 23, mapping road network, road section and parking space elements in a Geojson format into corresponding element objects in the analog by using a Python code: constructing a road network object at a corresponding position in an analog by using an XML format, embedding road section and parking space elements in the road network object, and creating a unique element name and element ID for the road network and auxiliary objects thereof;
step 24: automatically generating a parking space occupation element and a occupation control plug-in by using a Python code, and writing the occupation control plug-in into a corresponding position of a target surface element in the analog in an XML format;
and 25, manually adding or adjusting intersection elements and stop line elements for the automatically generated road network, and adding traffic lights to each pedestrian crossing channel to bind the pedestrian crossing channel with the corresponding stop line.
6. The method according to claim 1, wherein step S3 is specifically as follows:
step 31, setting slow-running main body action logic: the intelligent agent is generated from the edge of the sidewalk and the entrance and exit of the district, a target line sequence provided by a shortest path algorithm is used as a navigation medium, the intelligent agent is connected to each POI point in a preset route at the minimum distance cost, the intelligent agent stays on the corresponding POI point for a corresponding time, and finally the intelligent agent leaves the working area from the edge of the sidewalk or the entrance and exit of the district through all the POI points;
step 32, setting movement logic for motor vehicle groups: the motor vehicle is generated at the edge of a motor vehicle lane, the bus is set to stay or queue at a bus station, a part of non-bus motor vehicles stay at a selected parking space, and a part of non-bus motor vehicles directly pass through and leave a working area from the edge of the motor vehicle lane;
step 33, logic for setting pedestrian crossing and vehicle deceleration: when the pedestrians reach target lines on two sides of each pedestrian path, the vehicles reaching or approaching the parking lines are decelerated;
step 34, logic for setting the occupied space of the parking space to block pedestrian flow: when a vehicle enters a parking space, the occupied area corresponding to the parking space cannot enter, and after the motor vehicle leaves the parking space, the occupied area is opened to pedestrians again.
7. The method according to claim 1, wherein step S4 is specifically as follows:
step 41, constructing a Track event and a Record event to Record pedestrian space-time Track information;
step 42, writing the space-time track of the slow-line main body into the shape vector file in a time-division manner;
and 43, reading a shape file by using the ArcGIS, and calculating the occupied area of pedestrians on motor lanes at different times, the average population density on the sidewalk and the average moving speed of the slow-moving group.
8. A slow-going group safety state measuring and calculating system based on city slow-going traffic simulation is characterized by comprising the following specific steps:
a random route generation module for: parameterizing social slow-going group travel habits and generating a random route collection;
the road environment vector model module is used for: constructing a road environment vector model, and directly mapping GIS vector elements into an analog simulation model for manual adjustment;
a mobile logic building module for: constructing a slow-going group and motor vehicle movement logic: acquiring alternative paths of the slow-going group by utilizing a shortest path algorithm based on the walking network, the riding network and POI distribution of interest points in the area;
the congestion degree calculating and evaluating module is used for calculating and evaluating the congestion degree of the slow traffic: writing the alternative paths and the navigation target lines into an analog simulation template, designing operation logic for the slow-driving groups and the motor vehicles, and calculating the average moving speed, the space distribution density and the occupied area ratio index of the motor vehicle occupied by pedestrians of various slow-driving groups.
9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, wherein the instructions are executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.
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| CN116910853A (en) * | 2023-06-28 | 2023-10-20 | 深圳市蕾奥规划设计咨询股份有限公司 | Pedestrian travel simulation method and device for TOD three-dimensional slow-going space |
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| CN115620165A (en) * | 2022-11-07 | 2023-01-17 | 中国科学院空天信息创新研究院 | Method, device, equipment and medium for evaluating slow-moving system facilities of urban built-up area |
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| CN113158847A (en) * | 2021-04-07 | 2021-07-23 | 深圳市金泉和科技有限公司 | Unsafe behavior detection method based on artificial intelligence |
| CN115620165A (en) * | 2022-11-07 | 2023-01-17 | 中国科学院空天信息创新研究院 | Method, device, equipment and medium for evaluating slow-moving system facilities of urban built-up area |
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