CN115565368A

CN115565368A - Urban traffic simulation-oriented traffic demand rapid estimation method

Info

Publication number: CN115565368A
Application number: CN202211138801.5A
Authority: CN
Inventors: 张微; 冯天; 张继灵; 杨乐豪
Original assignee: Zhejiang University ZJU
Current assignee: Zhejiang University ZJU
Priority date: 2022-09-19
Filing date: 2022-09-19
Publication date: 2023-01-03

Abstract

The invention discloses a Traffic demand rapid estimation method for Urban Traffic Simulation, which is characterized in that an Urban area corresponding to a road network is divided into a plurality of Traffic Analysis Zones (TAZ) in a self-defined manner through road network data downloaded from an OpenStreetMap based on population density data of a target area, an OD matrix and the divided TAZ are combined, a Gravity model (Gravity model) is used for estimating the number of starting and ending vehicles among the TAZs according to the distance, the population number and the number of entrance and exit data among the TAZs, a corresponding Traffic demand file is rapidly generated, and then Simulation is carried out on the road network by utilizing a Simulation of Urban MObility (SUMO). The method can greatly reduce the time consumption and error rate caused by manual customization on the basis of ensuring the correlation between traffic demand and actual conditions, thereby improving the accuracy and reliability of urban traffic simulation by using SUMO.

Description

Urban traffic simulation-oriented traffic demand rapid estimation method

Technical Field

The invention relates to the technical field of urban traffic simulation, in particular to a traffic demand rapid estimation method for urban traffic simulation.

Background

Along with the process of urbanization and the continuous development of economic society, urban traffic becomes an important part of the lives of residents. The traffic network adaptive to the rapidly increased traffic demand has significant positive significance for the travel of residents. Planning and constructing a corresponding traffic network, and accurately simulating urban traffic on the basis of actual conditions. As an open source platform with wide influence, simulation of Urban MObility (SUMO) is an evaluation index that dynamically simulates how a vehicle travels in a road network according to a traffic demand set by a user and outputs a service further decision. Using SUMO for urban traffic simulation, at least two types of input data are required: road network files and traffic demand files. At present, the method for generating traffic demand for a specified road network mainly comprises: (1) Customizing an XML (road.xml) file of a vehicle path, wherein the XML file comprises information such as departure time, departure road sections and arrival road sections of all vehicles; the method needs a great deal of details when dealing with a complex road network, is very complicated and is easy to make mistakes; (2) generating traffic demands using a stochastic algorithm; the method generates results which are often not related to actual conditions; (3) Using Origin-Destination (OD) matrix to divide the city area corresponding to the road network into a plurality of Traffic Analysis Zones (TAZ), customizing the number of departure and arrival vehicles among different TAZs, and estimating Traffic demand; the method will face a lot of self-defining work along with the increase of the number of TAZs, and no reference index is used for evaluating whether the number of vehicles among the TAZs is close to reality or not. Therefore, based on population density data of the target area, the OD matrix and the divided TAZs are combined, the starting and ending vehicle numbers among the TAZs are estimated by using a Gravity model (Gravity model) according to the distances among the TAZs, the population numbers and the number of entrance and exit data, and a corresponding traffic demand file is generated quickly.

Disclosure of Invention

The invention aims to solve the technical problems that in the current SUMO urban traffic network simulation process, due to the fact that the traffic network topological structure is complex, the traffic requirement is difficult to construct rapidly and accurately so as to meet the real life, and the workload of artificially modeling the traffic requirement is large, the method for rapidly estimating the traffic demand for urban traffic simulation is provided.

The invention adopts the following specific technical scheme:

a traffic demand rapid estimation method for urban traffic simulation comprises the following steps:

s1, acquiring grid data of a population density map of a target city, and segmenting the grid data into population density sub-maps with the same size;

s2, obtaining OSM vector road network data corresponding to each population density sub-graph from the OpenStreetMap;

s3, according to a road network file format required by the SUMO designation of the urban traffic simulation platform, converting the OSM vector road network data obtained in the step S2 into a vector road network file designated by the SUMO by using an SUMO road network converter netconvert;

s4, dividing the vector road network file specified by the SUMO obtained in the step S3 into a plurality of traffic analysis zones TAZ, and generating a TAZ file for describing information of each TAZ;

s5, analyzing the vector road network file and the population density subgraph, and calculating the population number and the entrance and exit degree of each TAZ and the distance between any two TAZs;

and S6, calculating the number of passing vehicles between any two TAZs by adopting a Gravity model (Gravity model) according to the population number, the entrance and exit degree and the distance between any two TAZs, and generating an OD matrix file.

S7, converting the OD matrix file obtained in the step S6 into a vehicle travel file by adopting an SUMO application tool OD2trips so as to obtain the information of the initial road and the ending road of the vehicle;

s8, converting the vehicle travel files into vehicle routing files by adopting an SUMO application tool, so as to obtain road sections through which each vehicle travel passes;

and S9, combining the vehicle routing file obtained in the step S8 and the vector road network file, and completing simulation of the road network through a SUMO simulation tool SUMO.

Preferably, in the step S1, the population density map raster data is population census-based population density map raster data, and is divided into subgraphs with a resolution of 512 × 512 pixels by using Arcgis software, wherein each pixel has a size of 5 meters, and a unit of pixel value is ten thousand per square kilometer.

Preferably, in step S2, the step of obtaining OSM vector road network data corresponding to the population density subgraph includes: reading space reference system information corresponding to the population density subgraph by using a gdal library of Python, wherein the space reference system information comprises the coordinate of a pixel at the upper left corner, the pixel resolution and the row and column number of raster data, and calculating the coordinate of a pixel at the lower right corner; and then, taking the spatial reference system information corresponding to the population density subgraph as a parameter, acquiring the corresponding OSM vector road network data by using a graph _ from _ bbox method of an osmnx library of Python, and storing the OSM vector road network data as an OSM file.

Preferably, in step S3, the OSM vector road network data is converted into a SUMO vector road network file specified by the SUMO traffic simulation platform by using a command netconvert-OSM-files.

Preferably, in the step S4, the traffic analysis zone TAZ is described by its id and a list of source and target edges; dividing the SUMO vector road network file into a plurality of TAZs by adopting an OD matrix method and generating a TAZ file for describing each TAZ information, wherein the specific division steps are as follows: dividing the SUMO vector road network file with the resolution of 512 pixels into 16 TAZs and 16 TAZs, wherein the size of each TAZ is 32 pixels, traversing each junction road network node information in the SUMO vector road network file, calculating the TAZ to which the position belongs, adding the road segment ID connected with the junction road network node into the TAZ, and writing the road segment ID into the TAZ file, wherein the generated TAZ file comprises the ID of each TAZ and the information of the connected road segment.

Preferably, in the step S5, the specific steps of calculating the population density and the entrance/exit degree of each TAZ and the distance between any two TAZs are as follows: reading a population density sub-graph, multiplying each pixel value of the population density sub-graph by the area represented by each pixel to obtain the population number represented by each pixel value, and adding the population numbers represented by all the pixels on each TAZ to obtain the total population number of each TAZ; then, counting the sum of the degrees of all the connection nodes of each TAZ as the access degree of the TAZ; the euclidean distance between the center point pixels of the two TAZs is calculated as the distance between the two TAZs.

Preferably, in step S6, the specific steps of calculating the number of vehicles passing between any two TAZs by using a gravity model and generating an OD matrix file are as follows: in combination with the data obtained by statistics in step S5, the number of vehicles passing between the two TAZs should be in direct proportion to the population and the entrance and exit degree of the two TAZs and in inverse proportion to the distance between the two TAZs, so the calculation of the TAZs from the traffic analysis area is combined with the gravity model _i Traffic to traffic analysis zone TAZ _j Number of vehicles T _ij Comprises the following steps:

wherein: t is _ij Is TAZ _i To TAZ _j Number of vehicles of (A) _j Is TAZ _j Degree of entrance and exit, P _i And P _j Are each TAZ _i And TAZ _j Population of middle school, F _ij Is TAZ _i To TAZ _j All TAZs is the set of all traffic analysis zones; the IDs of any two TAZs and the number of passing vehicles are written into an OD matrix file, thereby generating an OD matrix file.

Preferably, in the step S7, the step of converting the OD matrix file obtained in the step S6 into the vehicle travel file by the SUMO application tool OD2 tries includes: the method comprises the steps of generating a vehicle trip file (od.xml) by using a command (OD 2 trips-c.cfg.xml-n.taz.xml-d.od.xml-o.xml) containing a configuration file (cfg.xml), a TAZ file (taz.xml) and an OD matrix file (od.od.xml), wherein the vehicle trip file contains departure information of each vehicle, and the saved contents of the vehicle trip file comprise vehicle id, departure time depart, departure road id, arrival road id, departure TAZ and arrival TAZ.

Preferably, in step S8, the converting the vehicle travel file into the vehicle routing file by the duarouter tool of the SUMO is specifically: the SUMO road network file net.xml and the vehicle routing file trips.xml are configured to the configuration file duarcfg.xml, and then a command duarouter-c.dumcfg.xml-o.rou.xml is used to calculate a route by using a Dijkstra algorithm to generate the vehicle routing file rou.xml, wherein the route file contains route information of each vehicle and edges of each road section ID passed by.

Preferably, in step S9, the simulation of the road network is completed by a SUMO command of the SUMO, wherein the command is a SUMO-gui-net-files net.

Compared with the prior art, the invention has the following beneficial effects:

Drawings

FIG. 1 is a general flow diagram of the process of the present invention;

FIG. 2 is an example of a traffic zoning model for a road network;

FIG. 3 is a graph of population density grid data for an example;

FIG. 4 illustrates an exemplary basic road network model;

fig. 5 is an exemplary diagram of a SUMO simulation part of a road network after traffic demands are completed in an example.

Detailed Description

The invention will be further elucidated and described with reference to the drawings and the detailed description. The technical characteristics of the embodiments of the invention can be correspondingly combined without mutual conflict.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. The technical characteristics in the embodiments of the present invention can be combined correspondingly without mutual conflict.

For convenience of description, a city in which a rapid estimation of traffic demand is required is referred to as a target city in the present invention.

As shown in fig. 1 to 5, the invention discloses a traffic demand fast estimation method for urban traffic simulation, which comprises the following steps:

s1, acquiring census-based population density map raster data of a target city, wherein the population density map raster data is often very large, so that the population density map raster data can be cut into sub-pictures with the same size for program processing, and the specific method comprises the following steps:

the population density map raster data was sliced into population density sub-maps of 512 x 512 pixels in size, each pixel approximately 5 meters in size, with pixel values in units of ten thousand people per square kilometer, using Arcgis software.

S2, obtaining OSM vector road network data corresponding to each population density sub-graph from the OpenStreetMap, wherein the specific method comprises the following steps:

GDAL (Geospatial Data Abstraction Library) is a basic Library for processing geographic grid Data (e.g.. Tif.) and vector Data (e.g.. Shp.), the bottom layer of the GDAL is realized by C/C + +, calling interfaces of Python, java and other languages are packaged, program codes are utilized to traverse each population density subgraph, and firstly, a GetGeoTransform method of the GDAL Library is utilized to obtain spatial reference system information corresponding to the population density subgraph, wherein the spatial reference system information comprises the longitude and latitude of a pixel at the upper left corner, the resolution of the pixel, the row number and the column number of the grid Data, and the like, so that the longitude and the latitude of a pixel at the lower right corner can be calculated; and then, taking the spatial reference system information corresponding to the population density subgraph as a parameter, acquiring the corresponding OSM vector road network data by using a graph _ from _ bbox method of an osmnx library of Python, and storing the OSM vector road network data as a star-OSM file. The OSMnx adopted in the invention is a Python package, is constructed on geopanands, network x and matplotlib, can download geographic data resources from OpenStreetMap, and performs modeling, projection, visualization, analysis and the like.

S3, because the Urban traffic Simulation platform (organization of Urban Mobility, SUMO) requires a road network file with a specified format, the SUMO road network converter netconvert is adopted to convert the OSM vector road network data obtained in the step S2 into a vector road network file (hereinafter referred to as SUMO vector road network file, referred to as SUMO road network file for short) specified by SUMO, and the specific conversion method is as follows:

the OS module in Python provides functions to interact with the operating system. System () method executes a command (string) in a child shell. This method is implemented by calling the standard C function system () and has the same limitations. The OSM vector road network data can be converted into a SUMO road network file available to the SUMO traffic simulation platform by calling an os.system () method through a code to execute a command netconvert-OSM-files. In the SUMO road network file, type tags are used for describing road type information, edge tags are used for describing road section information, junction fields are used for describing road network node information, and connections are used for describing connection relation information between road sections and between lanes.

And S4, in order to fully consider the difference of vehicle flow caused by the difference of population densities among the areas, dividing the vector road network file specified by the SUMO obtained in the step S3 into a plurality of traffic analysis areas and generating a TAZ file for describing each piece of TAZ information, wherein the specific method comprises the following steps:

the Traffic Analysis Zone (TAZ) is described by its id (arbitrary name) and a list of source and target edges. In the implementation process, the OD matrix method may be adopted to divide the SUMO vector road network file into several TAZs and generate TAZ files for describing information of each TAZ. The OD matrix is a matrix in which all TAZs are sorted by row (origin zone) and column (destination zone), and the travel (OD) of the residents or vehicles between any TAZ is an element. As shown in fig. 2, the SUMO network file with a resolution of 512 × 512 pixels is divided into 16 rows and 16 columns, i.e., 256 TAZs with the same size, the ID marks of the TAZs are sequentially marked from top to bottom and from left to right as 0-255, and the size of each TAZ is 32 × 32 pixels, so that the SUMO network file can be analyzed by using xml. When traversing each junction road network node in the SUMO road network, the number x of rows and the number y of columns of the junction in the original SUMO road network file can be judged through the coordinate information and the upper left-hand coordinate, the ID of the TAZ to which the junction belongs is calculated to be x + 16+ y, a List can be defined through Python for each TAZ to store the link ID set of the TAZ, the link IDs connected with the junction road network node are added into the List to which the TAZ belongs, and finally the List is written into the TAZ file, and the generated TAZ file contains the ID of each TAZ and the information of the connected links.

S5, because the traffic flow between the two TAZs is often dependent on the population density, the entrance and exit degree and the distance between the two TAZs, analyzing the SUMO road network file and the population density subgraph by utilizing a code program, calculating the population number, the entrance and exit degree of each TAZ and the distance between any two TAZs, and using the number, the entrance and exit degree and the distance between any two TAZs for subsequently calculating the traffic flow between the two TAZs, the specific method is as follows:

reading grid data of a population density map by using a cv2 library of Python, judging the row number x and the column number y of the corresponding TAZ according to the position of each pixel, wherein the ID of the corresponding TAZ is x + 16+ y, multiplying each pixel value of a population density subgraph by the area represented by each pixel to obtain the population number represented by each pixel value, and adding the population numbers represented by all the pixels on each TAZ to obtain the total population number of each TAZ; then, the sum of degrees of all connection nodes of each TAZ is calculated in a traversing mode to serve as the entrance and exit degrees of the TAZ; the distance between two TAZs is defined as the euclidean distance between the central point pixels thereof, and thus the distance between the two TAZs can be obtained by calculating the euclidean distance between the central point pixels thereof.

And S6, calculating the number of vehicles by adopting a Gravity model (Gravity model) according to the population number, the entrance and exit degrees and the distance between any two TAZs, and generating an OD matrix file. The gravity model is also called as a gravity model, and is a future traffic distribution prediction model established by applying the relationship that the number of outgoing lines in two intervals is in direct proportion to the outgoing occurrence amount of the departure area and the outgoing suction amount of the arrival area and in inverse proportion to the travel time (or cost, distance and the like) in the two intervals.

In this embodiment, the specific steps of calculating the number of vehicles passing between any two TAZs by using the gravity model and generating the OD matrix file are as follows: in combination with the data obtained by statistics in step S5, the number of vehicles passing between the two TAZs should be in direct proportion to the population and the entrance and exit degree of the two TAZs and in inverse proportion to the distance between the two TAZs, so the calculation of the TAZs from the traffic analysis area is combined with the gravity model _i Traffic to traffic analysis zone TAZ _i And TAZ _j Number of vehicles T _ij Comprises the following steps:

wherein: t is _ij Is TAZ _i To TAZ _j Number of vehicles of (A) _j Is TAZ _j Degree of entrance and exit, P _i And P _j Are each TAZ _i And TAZ _j Population of (5), F _ij Is TAZ _i To TAZ _j All TAZs is the set of all traffic analysis zones; and writing the IDs of any two TAZs and the number of vehicles directly passing through the IDs into an OD file by using the codes, thereby generating a corresponding OD matrix file.

And S7, in order to acquire information of the initial road, the final road and the like of the vehicle, converting the OD matrix file obtained in the step S6 into a vehicle travel file by adopting an SUMO application tool OD2trips, wherein the specific method comprises the following steps:

the SUMO application tool OD2 tries to separate the route information of a single vehicle by importing OD matrix data. The method comprises the steps of generating a cfg.xml configuration file by a code program, writing information of a TAZ file and an OD matrix file, and executing a command OD2 trips-c.cfg.xml-n.taz.xml-d.od.xml-o.trips.xml by an os.system () mode of Python, wherein the command OD2 trips-c.cfg.xml, the TAZ file and the OD matrix file are contained in the command od.xml, the vehicle trip file is generated from the cfg.xml.trips.xml, the vehicle trip file contains departure information of each vehicle, and the departure information mainly contains vehicle id (id), departure time depart (depart), origin road id (from), arrival road (id), departure TAZ (from Taz) and arrival TAZ (to Taz).

S8, in order to obtain the road sections passed by each vehicle travel, a SUMO application tool (duarouter) is adopted to convert the vehicle travel files into vehicle routing files, and the specific method comprises the following steps:

the SUMO application tool, durarioter, imports different requirement definitions, and the SUMO calculates the path of the vehicle to obtain the shortest calculated path. The xml is configured to the configuration file, duarcfg.xml, and then a command, duarout-c, duarcfg.xml, is used to calculate a route using Dijkstra algorithm to generate a corresponding vehicle route file, rou.xml, which contains route information of the vehicle and edge of each road segment ID passed by.

And S9, combining the vehicle routing file and the SUMO road network file obtained in the step S8, and completing simulation of the road network through a SUMO simulation tool (SUMO). The sumo command is a microscopic, spatially continuous, time-discrete simulation of traffic flow. sumo-gui is essentially the same as sumo, but is extended through the graphical user interface. The simulation comprises the following specific steps: sum-gui-net-files-net.xml-route-files-row.xml of SUMO, which may be executed by os.system () method of Python, contains sum road network file-net.xml and route file-row.xml. Therefore, accurate traffic flow can be generated quickly for SUMO simulation.

The traffic demand rapid estimation method for urban traffic simulation described in the above S1 to S9 is applied to the sydney urban traffic network to construct traffic demand, so as to show the technical effects that can be achieved by the method.

Examples

In this embodiment, the target city is sydney, and the traffic demand fast estimation method framework is as described in S1 to S9, which is not described in detail, and specific data and effects are mainly shown below.

1. Firstly, acquiring the grid data of a population density map of a Sydney city, and cutting the grid data into sub grid pictures with the same size through arcgis, wherein SY _ POP _588.GIF sub grid pictures are taken as an example, and the grid data is shown in a figure 3;

2. the space reference system information is obtained through a gdal library read by Python, the longitude of the upper left corner is 150.78527287568414, the latitude is-34.09100741424642, the longitude of the lower right corner is 150.81280777759991, the latitude is-34.11408896879206, and the osm vector road network file is obtained through a graph _ from _ bbox method of osmnx as a parameter and named as SY _ NET _588.

3. Executing a netconvert command by using os.system () method of Python to obtain a road network format file which is required by SUMO simulation and is named as SY _ NET _588.net.xml, and opening the road network file by using netedit as shown in fig. 4;

4. on the basis of SUMO road network SY _ NET _588.Net.xml data, a TAZ file named SY _ NET _588.Taz.xml is generated through a Python structure, and then an OD matrix file named SY _ NET _588.Od.xml is generated on the basis of population density map SY _ POP _588.GIF grid data.

5. And generating a vehicle travel file named SY _ NET _ 588.trip.xml by adopting an SUMO application tool (OD 2 tries) on the basis of the TAZ file SY _ NET _588.taz.xml and the OD matrix file SY _ NET _588.od.xml.

6. And on the basis of the vehicle travel file SY _ NET _588, trips.xml and the SUMO road network file SY _ NET _588, net.xml, an SUMO application tool (duarouter) command is adopted, and the vehicle travel file is converted into a vehicle routing file named SY _ NET _588, rou.xml based on a Dijkstra algorithm.

7. The traffic network is simulated by a SUMO simulation tool (SUMO-gui) on the basis of a vehicle routing file SY _ NET _588.rou.xml and a SUMO road network file SY _ NET _588.net.xml, and the specific part of simulation effect is shown in fig. 5.

The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention. One of ordinary skill in the pertinent art will recognize,

various changes and modifications can be made without departing from the spirit and scope of the invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.

Claims

1. A traffic demand rapid estimation method for urban traffic simulation is characterized by comprising the following steps:

s4, dividing the SUMO appointed vector road network file obtained in the step S3 into a plurality of traffic analysis zones TAZ, and generating a TAZ file for describing each TAZ information;

and S6, calculating the number of vehicles passing between any two TAZs by adopting a Gravity model (Gravity model) according to the population number, the entrance and exit degrees and the mutual distance of any two TAZs, and generating an OD matrix file.

S7, converting the OD matrix file obtained in the step S6 into a vehicle travel file by adopting an SUMO application tool OD2trips so as to obtain information of a starting road and an ending road of the vehicle;

2. The method for rapidly estimating traffic demand oriented to urban traffic simulation according to claim 1, wherein in step S1, the population density map raster data is population census-based population density map raster data, and is sliced into subgraphs with a resolution of 512 × 512 pixels by using Arcgis software, each pixel has a size of 5 meters, and a pixel value has a unit of ten thousand people per square kilometer.

3. The method for rapidly estimating traffic demand for urban traffic simulation according to claim 1, wherein in step S2, the method for obtaining OSM vector road network data corresponding to population density subgraphs specifically comprises: reading space reference system information corresponding to the population density subgraph by using a gdal library of Python, wherein the space reference system information comprises the coordinate of a pixel at the upper left corner, the pixel resolution and the row number and column number of grid data, and calculating the coordinate of a pixel at the lower right corner; and then, taking the spatial reference system information corresponding to the population density subgraph as a parameter, acquiring the corresponding OSM vector road network data by using a graph _ from _ bbox method of an osmnx library of Python, and storing the OSM vector road network data as an OSM file.

4. The method as claimed in claim 1, wherein in step S3, using a command netconvert-orm-files, orm-o, net, xml, to convert OSM vector road network data into a SUMO vector road network file specified by a SUMO traffic simulation platform, where the SUMO vector road network file uses type tags to describe road type information, edge tags to describe road section information, junction fields to describe road network node information, and connections to describe connection relationship information between road sections and lanes.

5. The method for rapidly estimating traffic demand oriented to urban traffic simulation according to claim 1, wherein in step S4, the traffic analysis zone TAZ is described by its id and the list of source edge and target edge; dividing the SUMO vector road network file into a plurality of TAZs by adopting an OD matrix method and generating a TAZ file for describing each TAZ information, wherein the specific division steps are as follows: dividing the SUMO vector road network file with the resolution of 512 pixels into 16 TAZs and 16 TAZs, wherein the size of each TAZ is 32 pixels, traversing each junction road network node information in the SUMO vector road network file, calculating the TAZ to which the position belongs, adding the road segment ID connected with the junction road network node into the TAZ, and writing the road segment ID into the TAZ file, wherein the generated TAZ file comprises the ID of each TAZ and the information of the connected road segment.

6. The method for rapidly estimating the traffic demand oriented to the urban traffic simulation as claimed in claim 1, wherein in the step S5, the specific steps of calculating the population density and the entrance and exit degree of each TAZ and the distance between any two TAZs are as follows: reading the population density sub-graph, multiplying each pixel value of the population density sub-graph by the area represented by each pixel to obtain the population number represented by each pixel value, and adding the population numbers represented by all pixels on each TAZ to obtain the total population number of each TAZ; then, counting the sum of the degrees of all the connection nodes of each TAZ as the access degree of the TAZ; the euclidean distance between the center point pixels of the two TAZs is calculated as the distance between the two TAZs.

7. The method for rapidly estimating the traffic demand oriented to the urban traffic simulation, as claimed in claim 1, wherein in step S6, the specific steps of calculating the number of vehicles passing between any two TAZs by using a gravity model and generating an OD matrix file are as follows: data obtained by statistics in combination with step S5The number of vehicles passing between the two TAZs is proportional to the population and the entrance and exit degrees of the two TAZs and is inversely proportional to the distance between the two TAZs, so that the traffic analysis zone TAZ is calculated by combining a gravity model _i Traffic to traffic analysis zone TAZ _j Number of vehicles T _ij Comprises the following steps:

wherein: t is a unit of _ij Is TAZ _i To TAZ _j Number of vehicles of A _j Is TAZ _j Degree of entrance and exit, P _i And P _j Are each TAZ _i And TAZ _j Population of middle school, F _ij Is TAZ _i To TAZ _j All TAZs is the set of all traffic analysis zones; the IDs of any two TAZs and the number of vehicles passing through the TAZ are written into an OD matrix file, thereby generating the OD matrix file.

8. The method as claimed in claim 1, wherein in step S7, the step of converting the OD matrix file obtained in step S6 into the vehicle travel file by using SUMO application tool OD2trips includes the specific steps of: the method includes the steps that a command OD2trips-c cfg.xml, a TAZ file taz.xml and an OD matrix file od.xml are used, wherein the command OD2trips-c cfg.xml-n taz.xml-d x od.xml-o.xml generates a vehicle travel file which contains departure information of each vehicle and stores the departure information including vehicle id, departure time, departure road id, arrival road id, departure TAZ and arrival TAZ.

9. The method as claimed in claim 1, wherein in step S8, the means of duarouter of SUMO converts the vehicle travel file into a vehicle routing file, specifically: the SUMO road network file net.xml and the vehicle routing file trips.xml are configured to the configuration file duarcfg.xml, and then a command duarouter-c.dumcfg.xml-o.rou.xml is used to calculate a route by using a Dijkstra algorithm to generate the vehicle routing file rou.xml, wherein the route file contains route information of each vehicle and edges of each road section ID passed by.

10. The method of claim 1, wherein in step S9, the simulation of the road network is completed by a SUMO command of the SUMO, where the command is SUMO-gui-net-files.net.xml-route-files.rou.xml, and the command includes SUMO road network files.net.xml and vehicle route files.rou.xml.