CN114462233A

CN114462233A - Microscopic traffic simulation method, computer device and storage medium

Info

Publication number: CN114462233A
Application number: CN202210097926.1A
Authority: CN
Inventors: 黄磊; 杨昀霖; 孙威巍
Original assignee: Guangzhou Fangwei Smart Brain Research And Development Co ltd
Current assignee: Guangzhou Fangwei Smart Brain Research And Development Co ltd
Priority date: 2022-01-18
Filing date: 2022-01-27
Publication date: 2022-05-10

Abstract

The invention discloses a microscopic traffic simulation method, a computer device and a storage medium, wherein the microscopic traffic simulation method comprises the steps of obtaining calculable road network data, vehicle travel record data and signal lamp state data, converting the calculable road network data into adaptive road network data, and converting the vehicle travel record data into directed sub-road section sequence path data; and (3) calculating the signal lamp state data and the calculable road network data to obtain signal control scheme data, operating simulation software, and simulating according to the adapted road network data, the sequence path data of the directed sub-road sections and the signal control scheme data. The invention has higher simulation accuracy, simulation precision and reduction degree of actual traffic state, can avoid the problem caused by operation negligence during artificial simulation modeling, can automatically realize the data collection and calculation of simulation results, finally provides visual evaluation results in various forms, and realizes the service of microscopic traffic simulation on traffic engineering. The invention is widely applied to the technical field of traffic control.

Description

Microscopic traffic simulation method, computer device and storage medium

Technical Field

The invention relates to the technical field of traffic control, in particular to a microscopic traffic simulation method, a computer device and a storage medium.

Background

The traffic simulation is to use the computer technology to perform space-time reproduction and rehearsal on the road traffic running condition, and because the implementation cost of many practical traffic problems is too high, even some scenes cannot be truly tested, the traffic simulation technology can solve the problem, and has higher practical value and better application prospect.

At present, the micro traffic simulation business software which is widely applied mostly needs to manually draw and create a road network which is suitable for specific software or even specific software versions during modeling, the manual drawing of the road network can consume a large amount of time of modeling personnel, the road network which is manually created by the modeling personnel can be different from person to person in details, the micro traffic simulation is very sensitive to the road network details, the data input of the modeling mode can consume a large amount of time, the road network and the input data are not easy to modify, the micro traffic simulation business software can not be suitable for large-scale road network modeling, and the standardization and multi-scene comparison are not facilitated.

Disclosure of Invention

The invention aims to provide a microscopic traffic simulation method, a computer device and a storage medium, aiming at least one technical problem that a vehicle cannot be tracked under the condition that positioning data cannot be obtained.

In one aspect, an embodiment of the present invention includes a micro traffic simulation method, including:

the method comprises the steps of obtaining calculable road network data, vehicle travel record data and signal lamp state data;

converting the calculable road network data into adapted road network data; the adaptive road network data is adapted to the input format of the simulation software;

converting the vehicle travel record data into directed sub-road section sequence path data;

calculating the signal lamp state data and the calculable road network data to obtain signal control scheme data;

and operating the simulation software, and simulating according to the adaptive road network data, the directed sub-road section sequence path data and the signal control scheme data.

Further, the converting the calculable road network data into adapted road network data includes:

extracting sub road section tables, lane tables, node tables, intersection tables and lane connector tables from the calculable road network data;

converting the sub-road section table, the lane table and the node table into a first file;

converting the node table and the intersection table into a second file;

converting the lane connector table into a third file;

and merging and converting the first file, the second file and the third file into the adaptive road network data.

Further, the converting the vehicle travel record data into directed sub-segment sequence path data includes:

setting a target space-time range;

screening out the vehicle travel record data meeting the target space-time range;

sequencing the road sections in the vehicle travel record data according to corresponding vehicle road section entering time to obtain road section sequence path data;

splitting the road sections without space continuity or time continuity in the road section sequence path data to obtain a plurality of sub road sections;

and composing the directed sub-road section sequence path data by the non-split road section and each split sub-road section.

Further, the splitting the road segment without spatial continuity or temporal continuity in the road segment sequence path data to obtain a plurality of sub-road segments includes:

when the previous road section is not upstream of the next road section in any two adjacent road sections in the road section sequence path data, determining that the next road section does not have spatial continuity, and splitting the next road section into a plurality of sub-road sections;

when the vehicle leaving time corresponding to the previous road section is different from the vehicle entering time corresponding to the subsequent road section in any two adjacent road sections in the road section sequence path data, determining that the subsequent road section does not have time continuity, and splitting the subsequent road section into a plurality of sub-road sections.

Further, the calculating the signal lamp state data and the calculable road network data to obtain signal control scheme data includes:

screening records of a selected time period according to a green light starting time field of the light state table, restricting the query range of the intersection number field, and querying signal control parameters from signal light state data;

separating the signal control parameters according to intersections as units, independently calculating green light time and yellow light of each phase in each period for each intersection, taking each period as an independent control scheme, distributing a scheme id associated with the intersection to each period, and compiling according to the period starting and ending time to obtain switching plan data;

extracting a signal control information table and a lane group of each signal control intersection from the computable road network data, reading a signal control index number corresponding to a connector of the intersection, matching the lane group with the signal control index number to obtain the lane group corresponding to the signal control index number, and combining the release rule data of each phase;

and merging the switching plan data and the release rule data to obtain the signal control scheme data.

Further, the running of the simulation software to perform simulation according to the adapted road network data, the directed sub-road segment sequence path data and the signal control scheme data includes:

merging the adaptive road network data, the directed sub-road segment sequence path data and the signal control scheme data to obtain merged data;

setting simulation parameters of the simulation software;

and operating the simulation software, calling the merged data for simulation, and outputting a simulation result file.

Further, the micro traffic simulation method further comprises the following steps:

according to the simulation result file, counting a plurality of traffic state operation indexes;

and according to the statistical result of the traffic state operation indexes, comprehensively evaluating the traffic state operation indexes according to a point line plane, and outputting a simulation report.

Further, the simulation software is SUMO simulation software.

In another aspect, the present invention further includes a computer device including a memory for storing at least one program and a processor for loading the at least one program to perform the microscopic traffic simulation method of the embodiments.

In another aspect, embodiments of the present invention also include a storage medium having stored therein a processor-executable program, which when executed by a processor, is configured to perform the microscopic traffic simulation method of the embodiments.

The invention has the beneficial effects that: according to the microscopic traffic simulation method in the embodiment, a fully-automatic program is used for converting the urban traffic high-precision computable road network into road network data matched with simulation software, manual intervention is not needed, only a specified simulation modeling area needs to be selected in a frame in the computable road network, the simulation road network is established quickly and efficiently, and compared with a manual drawn road network, a large amount of time can be saved and the modification is convenient; based on the identity detection data of the individual vehicles, the full-time full-volume vehicle individual-level outgoing data is used as the traffic input requirement of simulation software, and compared with the traditional modeling method that only the road section flow and the steering ratio after sample expansion are used as the requirement input, the accuracy and the simulation precision are higher; the real-time lamp state data of the global city signal lamp is used as the signal control input of simulation software, so that the reduction degree of the real traffic state is higher; after the simulation software completes automatic conversion, the other program can automatically complete the establishment of the lane-level virtual detectors covering all road sections based on the existing road network, manual establishment as in the traditional modeling is not needed, the problems of detector layout omission and the like caused by negligence of modeling personnel can be avoided, and modification according to different requirements is facilitated; the traffic running state evaluation is carried out based on the multi-class virtual detectors and the evaluation algorithm, the data collection and calculation of simulation results can be automatically realized, and finally, visual evaluation results are provided in various forms, so that the decision can be made according to the evaluation results, and the service of microscopic traffic simulation on traffic engineering is realized.

Drawings

FIG. 1 is a flow chart of a micro traffic simulation method in an embodiment;

FIG. 2 is a diagram illustrating a structure and a processing procedure for calculating road network data according to an embodiment;

fig. 3 is a schematic diagram of a step of splitting a road segment without spatial continuity or temporal continuity in road segment sequence path data to obtain a plurality of sub-road segments in the embodiment;

FIG. 4 is a schematic diagram illustrating the boundary direction of the detection region in the embodiment;

FIG. 5 is a diagram illustrating an exemplary research horizon calculable road network segment level;

FIG. 6 is a schematic diagram of an example research scope computable road network lane layer and lane connector layer;

FIG. 7 is a schematic diagram illustrating a SUMO road network after a research range conversion;

FIG. 8(a) is a schematic diagram of intersection geometry and first phase traffic rules in a default transition result for an exemplary signalized intersection;

FIG. 8(b) is a schematic diagram of a phase configuration in a default transition result for an exemplary signalized intersection;

FIG. 9(a) is a schematic diagram of intersection geometry and first phase traffic rules in a scenario in which an exemplary signalized intersection is replaced with an actual light state transition;

fig. 9(b) is a schematic diagram of a phase configuration in a scheme in which an example signalized intersection is replaced with an actual light state transition.

Detailed Description

In this embodiment, referring to fig. 1, the micro traffic simulation method includes the following steps:

s1, computable road network data, vehicle travel record data and signal lamp state data are obtained;

s2, converting the calculable road network data into adaptive road network data; adapting the road network data to an input format of simulation software;

s3, converting the vehicle travel record data into directed sub-road section sequence path data;

s4, calculating the signal lamp state data and the calculable road network data to obtain signal control scheme data;

and S5, operating simulation software, and simulating according to the adaptive road network data, the sequence path data of the directed sub-road sections and the signal control scheme data.

The steps S1-S5 may be performed by a computer. In this embodiment, the simulation software used is SUMO (simulation of Urban mobility) simulation software, each step in the micro traffic simulation method is described by taking the use of SUMO as an example, and the micro traffic simulation method in this embodiment may be simulated by using other types of simulation software with reference to the use mode of SUMO.

Prior to performing steps S1-S5, the simulation modeling area may be determined. Specifically, after the modeling area is determined according to actual requirements, all logical object data sets within the target range can be screened as input for subsequent road network automatic conversion.

In step S1, the computer obtains calculable road network data, vehicle travel record data and signal lamp status data. Referring to fig. 2, the basic logical objects that can calculate the road network data include nodes, directed road segments, directed sub-road segments, lanes, and lane connectors; the main fields of the refined individual vehicle travel record data comprise a vehicle ID, a vehicle type, a road section entering time, a road section leaving time, a current road section ID, an upstream road section ID, a downstream road section ID and the like; the signal lamp state data field mainly comprises intersection ID, entrance lane direction, lane group steering, green light starting time, green light ending time, scheme number, phase sequence number and the like.

When the computer executes step S2, that is, the step of converting the calculable road network data into the adapted road network data, the computer specifically executes the following steps:

s201, extracting a sub road section table, a lane table, a node table, an intersection table and a lane connector table from the calculable road network data;

s202, converting the sub-road section table, the lane table and the node table into a first file;

s203, converting the node table and the cross table into a second file;

s204, converting the lane connector table into a third file;

and S205, combining and converting the first file, the second file and the third file into the adaptive road network data.

In step S201, referring to fig. 2, the computer extracts data such as a sub road segment table, a lane table, a node table, an intersection table, and a lane connector table from the calculable road network data. In fig. 2, the road network architecture of the SUMO is two layers, namely, a road section (edge) and a lane (lane), and corresponds to an oriented sub-road section layer and a lane layer capable of calculating the road network.

In step S202, link (edge) conversion is performed. In this embodiment, the geometry of the road segments in the SUMO is represented by using a geometric shape capable of calculating a road network sub-road segment layer, the end points represent connection relationships, other attributes such as id and type of a lane set included in each sub-road segment are read, the other attributes include whether a lane allows a specific vehicle type, a lane speed limit, whether a lane change can be performed leftwards or rightwards, and other special settings, wherein whether the lane change of the specific vehicle type is allowed corresponds to a white solid line function in a real traffic rule, three tables of the sub-road segments, the lanes and the nodes capable of calculating the road network are converted into edge.

In step S203, node (node) conversion is performed. Since the node of the computable road network only represents the end point of the sub-link and is not necessarily the intersection in the SUMO road network concept, the related information of the intersection table specially representing the intersection needs to be used, the information of the two tables is used to convert the intersection table into the independent nodes.

In step S204, connector (connection) conversion is performed. Since the concept of the connector (connection) in the SUMO and the concept of the lane connector in the calculable road network are completely consistent and both represent a one-to-one connection relationship of different lanes, the lane connector table is simply processed to obtain the con.con.xml file, which is the third file obtained by performing the conversion in step S204.

After obtaining the three types of independent SUMO road network elements of the first file, the second file and the third file, the xml file, in step S205, the computer may execute a conversion program to call a netconvert program built in the SUMO, and convert the three files of the first file, the second file and the third file into adapted road network data capable of being adapted to the input format of the simulation software, specifically, the adapted road network data may be a demo. The conversion program may also be configured with some optional parameters, such as key parameters, such as no-internal-links (whether to establish a connection section in the intersection), roundabout, gusss (whether to establish a roundabout by inference from an internal algorithm), etc., and the controlled intersection may automatically generate a default signaling scheme during the conversion process, and may be modified through subsequent steps if the signaling scheme does not actually match the roundabout.

The converted road network file has no change in geometric characteristics such as road sections, lanes, intersection shapes and the like, and a detector covering the whole road network can be created based on the change. The lane length in the edges.edg.xml file and the lane length in the converted edges.net.xml file may be inconsistent, and the information such as the lane length of the edges.net.xml file needs to be read through a sumoli interface of the SUMO so as to complete the creation of the virtual detector at the proper lane position, and finally the detector is written into a SUMO attachment file of the edges.add.xml file.

In this embodiment, when the computer executes step S3, that is, the step of converting the vehicle travel record data into directional sub-link sequence path data, the computer specifically executes the following steps:

s301, setting a target space-time range;

s302, screening vehicle travel record data meeting a target space-time range;

s303, sequencing road sections in the vehicle travel record data according to corresponding vehicle road section entering time to obtain road section sequence path data;

s304, splitting road sections without space continuity or time continuity in the road section sequence path data to obtain a plurality of sub road sections;

s305, the road sections which are not split and the sub-road sections obtained through splitting form directed sub-road section sequence path data.

The used individual vehicle travel record data is based on the arrival-departure time record of the directed road section level, a target space-time range is set in step S301, and travel data of the target space-time range can be screened according to the determined simulation modeling range and the determined time period in step S302.

In step S303, the main fields of the refined individual vehicle travel record data include a vehicle ID, a vehicle type, a time to enter a road segment, a time to leave a road segment, a current road segment ID, an upstream road segment ID, a downstream road segment ID, and the like, and the road segments in the vehicle travel record data are sorted according to the corresponding time to enter the road segment by the vehicle, so as to obtain road segment sequence path data. Specifically, all records of each vehicle are sorted according to the entering time and then combined into a plurality of sequences including an entering time sequence, a leaving time sequence, an upstream road section sequence and a current road section sequence, so that the obtained road section sequence path data includes the entering time sequence, the leaving time sequence, the upstream road section sequence and the current road section sequence.

When the computer performs step S304, that is, the step of splitting the road segment without spatial continuity or temporal continuity in the road segment sequence path data to obtain a plurality of sub-road segments, the computer specifically performs the following steps:

s30401, when any two adjacent road sections in the road section sequence path data are not upstream of a following road section, determining that the following road section does not have spatial continuity, and splitting the following road section into a plurality of sub-road sections;

s30402, when the departure time of the vehicle corresponding to the previous road segment is different from the entry time of the vehicle corresponding to the subsequent road segment in any two adjacent road segments in the road segment sequence path data, determining that the subsequent road segment does not have time continuity, and splitting the subsequent road segment into a plurality of sub-road segments.

The principle of steps S30401-S30402 is shown in FIG. 3. Referring to fig. 3, each link in the link-sequential path data is traversed, and in one cycle shown in fig. 3, a "current link" and a "previous link" are two adjacent links, where the "current link" is a subsequent link and the "previous link" is a previous link. Firstly, judging whether the current road section has spatial continuity or not, specifically, if the previous road section is not upstream of the following road section, determining that the following road section does not have spatial continuity, and splitting the current road section, namely the following road section into a plurality of sub-road sections after determining that the following road section does not have spatial continuity; then, it is determined whether the "current road segment" has time continuity, specifically, if the departure time of the vehicle corresponding to the previous road segment is different from the entry time of the vehicle corresponding to the next road segment, it may be determined that the next road segment does not have time continuity, and the "current road segment", that is, the next road segment, is split into a plurality of sub-road segments, and if the same "current road segment" has been split into a plurality of sub-road segments in step S30401, each sub-road segment split into the "current road segment" may be split again in step S30402, and each sub-road segment is split into a plurality of sub-road segments.

After the completion of the splitting of the link in the link sequence path data in step S304, referring to fig. 3, for the link that has been split into sub-links, only the sub-links (equivalent to replacing the link before the splitting with the sub-link obtained by the splitting) are retained, and in step S305, the directed sub-link sequence path data is composed of the non-split link and the sub-links obtained by the splitting.

After steps S301-S305 are performed, the starting sub-segment may also be modified. Because the travel records cannot enter and leave from the road network boundary, the vehicle travel entering and leaving from the boundary does not need to be processed, and other situations need to be corrected, so that the condition that the vehicle enters and leaves the road network at the intersection in the simulation process and deadlock is caused is prevented. The principle of the correction is as follows: the entering position of the vehicle which does not enter from the boundary moves forward for a certain distance on the current road section, and the departure time is also delayed according to the average speed of the current time period of the current road section in the database, and the process is assumed to be uniform motion. Similarly, the distance offset of the vehicle which does not leave the road network from the boundary should not be the tail end of the entrance of the intersection, and the distance offset of the vehicle which enters and leaves the road network in the correction process needs to be correspondingly advanced by a certain distance.

After steps S301-S305 are performed, other property and requirements files may also be edited. Because the demand data of the SUMO needs to be written into the xu.xml file, the xu.xml file also comprises attribute data of different vehicle types besides the vehicle travel record attribute, the attribute data can control the driving behavior of the vehicle in the simulation process, mainly comprise various parameters of a following model and a lane changing model, and the data can be written according to basic preset parameters.

In this embodiment, when the computer performs step S4, that is, the computer performs the following steps when the signal lamp status data and the calculable road network data are calculated to obtain the signal control scheme data:

s401, screening records of a selected time period according to a green light starting time field of a light state table, restricting the query range of an intersection number field, and querying signal control parameters from signal light state data;

s402, separating the signal control parameters according to intersections as units, independently calculating green light time and yellow light of each phase in each period for each intersection, taking each period as an independent control scheme, distributing a scheme id associated with the intersection to each period, and compiling according to the period starting and ending time to obtain switching plan data;

s403, extracting a signal control information table and a lane group of each signal control intersection from the computable road network data, reading a signal control index number corresponding to a connector of the intersection, matching the lane group with the signal control index number to obtain the lane group corresponding to the signal control index number, and combining release rule data of each phase;

and S404, combining the switching plan data and the release rule data to obtain signal control scheme data.

Referring to fig. 4, in step S401, the computer screens records of a selected time period according to the green light start time field of the light state table, and simultaneously restricts the query range of the intersection number field, thereby obtaining the signal control parameters from the signal light state data query.

In step S402, the information control parameters queried in step S401 are separated and processed according to intersections as units, each intersection independently calculates green time and yellow time of each phase in each period, each period is used as an independent control scheme, a scheme id associated with the intersection is assigned, and switching plan data is compiled according to the period start/end time.

In step S403, the lane group and the information control information table of each information control intersection are read from the computable road network, the information control index number corresponding to the connector of the intersection is read through the sumoli interface provided by the SUMO, and the lane group corresponding to the information control index is obtained through matching the lane group with the information control index number, so as to combine the release rule data of each phase.

The basic data of the signaling and control scheme under the SUMO road network logic is obtained through the processing of the steps, and the basic data comprises switching plan data and release rule data. In step S404, writing the basic data of the signaling control scheme such as the switching plan data and the release rule data into the SUMO according to the proprietary format of the SUMO, wherein each period corresponds to an xml file element tlLogic, and finally, a signal.

By using the signal lamp state data and the lane group signal control information table capable of calculating the road network in the steps S401-S404 to calculate the timing period control scheme of each period, the lamp state control can be accurate to the second level, and the offset between the simulation starting time and the starting time of the signal control scheme is accurate. However, the conventional modeling method only uses a small number of fixed timing schemes, and cannot accurately restore control schemes with dynamic changes such as SCATS, and the conversion schemes in steps S401 to S404 in this embodiment can solve the problems encountered in the conventional modeling.

In this embodiment, when the computer executes step S5, that is, runs the simulation software, and performs the simulation according to the adapted road network data, the directed sub-road segment sequence path data, and the signal control scheme data, the computer specifically executes the following steps:

s501, combining adaptive road network data, sequence path data of directed sub-road sections and signal control scheme data to obtain combined data;

s502, setting simulation parameters of simulation software;

and S503, running simulation software, calling the merged data to carry out simulation, and outputting a simulation result file.

In step S501, adapted road network data (road network file demo.net.xml), directed sub-road segment sequence path data (demand file demand.rou.xml), and signal control scheme data (detector and signaling control scheme attachment file) are merged into one attachment file to obtain merged data add _ all.add.xml.

In step S502, add _ all. add.xml of the merged data may be configured into an input element of the sumocfg file, and parameters such as the remaining log, whether to move the vehicle out after collision, and the like may be configured as needed.

After the demo.sumocfg file is configured in step S502, step S503 may be executed to run the SUMO simulation, and the SUMO-gui command with the user operation interface and the silent command SUMO without the interface may be selected according to actual requirements for running the simulation.

After the steps S501-S503 are executed, all simulation result files output by the SUMO are stored as an xml file, the xml file includes detection results of various detectors, a total amount of vehicle tracks, and the like, and after the simulation is finished, the program automatically reads data in the xml file, converts the data into a database table format, and stores the data in the database.

After performing steps S1-S5, the computer has obtained the xml file representing the simulation result of the SUMO, and the computer may continue to perform the following steps:

s6, counting a plurality of traffic state operation indexes according to the simulation result file;

and S7, comprehensively evaluating the traffic state operation indexes according to the point-line plane according to the statistical result of the traffic state operation indexes, and outputting a simulation report.

In step S6, after the result data is stored in the database, statistics of various indexes, such as traffic flow, average vehicle speed, traffic efficiency, queue length, number of stops at intersections, stop rate, etc., are completed through a statistical procedure, wherein a logical matching relationship of the calculable road network is required to be used based on the result of the calculable road network segment layer.

In step S7, according to the selected traffic state operation index, the comprehensive evaluation is performed according to the "point-line-plane" hierarchy, a corresponding report is output, and export of various formats is supported, such as text files of excel data sheet, csv, and the like, and statistical results are also stored in the database, which is convenient for other programs to read.

In the microscopic traffic simulation method in the embodiment, the Urban traffic high-precision computable road network and refined fusion data are used for realizing full-automatic rapid simulation scene modeling and traffic running state evaluation, and the used simulation software can be open-source traffic simulators such as microscopic traffic simulation software SUMO (simulation of Urban mobility) and the like, so that the microscopic traffic simulation method has the advantage of low application cost. Compared with the existing simulation modeling technology, the microscopic traffic simulation method in the embodiment has the following advantages and characteristics:

(1) the microscopic traffic simulation method in the embodiment uses a full-automatic program to convert the urban traffic high-precision computable road network into the SUMO road network, does not need manual intervention, only needs to select a designated simulation modeling area in a frame in the computable road network, quickly and efficiently creates a simulation road network, and can save a large amount of time and be convenient to modify compared with a manual drawn road network;

(2) the microscopic traffic simulation method in the embodiment is based on the identity detection data (such as vehicle data detected by a card-type electric police) of individual vehicles, integrates the GPS records of taxis, network appointment vehicles and other operating and non-operating vehicles, extracts the complete travel path of each vehicle, comprises complete and rich travel records of the entry and exit time of each road section on the path, takes the full-time and full-volume vehicle individual-level travel data as the traffic input requirement of the SUMO, only uses the road section flow and steering ratio after sample expansion as the requirement input relative to the traditional modeling, and is higher in accuracy and simulation precision;

(3) considering that the influence of traffic signal lamps on traffic states is very critical, most of the traditional modeling methods use a fixed-period scheme based on short-time survey data as a signal control input scheme, the deviation between the lamp states of the signal lamps and the arrival of actual vehicles at the same moment is large, and the SCATS dynamic adjustment control scheme cannot be truly restored;

(4) in the microscopic traffic simulation method in the embodiment, after the SUMO road network is automatically converted, another program can automatically create lane-level virtual detectors covering all road sections based on the existing road network, manual creation as in the traditional modeling is not needed, the problems of detector layout omission and the like caused by negligence of modeling personnel can be avoided, and modification according to different requirements is facilitated;

(5) in the microscopic traffic simulation method in the embodiment, the traffic running state is evaluated based on the multiple types of virtual detectors and the evaluation algorithm, the data collection and calculation of the simulation result are automatically realized, and the evaluation result is finally provided in multiple forms.

In order to verify the technical effect of the micro traffic simulation method in the embodiment, an example research range is selected as an actual road network of a certain region, wherein a calculable road network segment layer is shown in fig. 5, a lane layer and a lane connector layer are shown in fig. 6, and a converted regional SUMO road network is shown in fig. 7. Fig. 8 and 9 are respectively the geometry and the configuration of the signaling scheme after the transition of the same exemplary intersection, wherein fig. 8 is the default signaling scheme at the road network transition stage, which is clearly unrealistic, because the right turn of the intersection is not controlled at all, and referring to the phase shown in fig. 8(b), fig. 8(a) shows that the right turn of the south entrance is forbidden at the first phase, as shown in fig. 9(a) and 9(b), the signaling scheme obtained with the light state data is consistent with the reality.

The road section bidirectional channel in the rectangular frame in the figure 5 is taken as a checking target, the east-west travel time and the west-east 217.49 of the simulation result are 161.58s and 170.18s respectively, 215.87s respectively, the east-west error and the west-east error are 5.05 percent and less than 1 percent respectively, and the flow error of the turning vehicle at each intersection within one hour is also controlled within 10 percent, which indicates that the invention has good error control and higher simulation precision. The overall process flow for this case takes about 2 minutes (including the runtime of the simulation module itself), and multiple road network tests on a larger scale do not exceed 5 minutes.

As can be seen from the verification processes shown in fig. 5 to 9, the calculation efficiency of the micro traffic simulation method in the embodiment meets the application requirements of real-time evaluation and preview, and a good application effect can be obtained.

The same technical effects as those of the micro traffic simulation method in the embodiment can be achieved by writing a computer program for executing the micro traffic simulation method in the embodiment, writing the computer program into a computer device or a storage medium, and executing the micro traffic simulation method in the embodiment when the computer program is read out and run.

It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this embodiment, the term "and/or" includes any combination of one or more of the associated listed items.

It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided with this embodiment is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.

It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.

Further, operations of processes described in this embodiment can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in this embodiment (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.

Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.

A computer program can be applied to input data to perform the functions described in the present embodiment to convert the input data to generate output data that is stored to a non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.

The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The technical solution and/or the embodiments thereof may be variously modified and varied within the scope of the present invention.

Claims

1. A microscopic traffic simulation method, characterized in that the microscopic traffic simulation method comprises:

converting the vehicle travel record data into path data formed by directional sub-road segment sequences;

2. The micro traffic simulation method according to claim 1, wherein the converting the calculable road network data into adapted road network data comprises:

converting the node table and the intersection table into a second file;

converting the lane connector table into a third file;

3. The micro traffic simulation method according to claim 1, wherein the converting the vehicle travel record data into directed sub-segment sequence path data comprises:

setting a target space-time range;

4. The micro traffic simulation method according to claim 3, wherein the splitting of the road segment without spatial continuity or temporal continuity in the road segment sequence path data to obtain a plurality of sub-road segments comprises:

5. The micro traffic simulation method according to claim 1, wherein the calculating the signal control scheme data from the signal lamp status data and the calculable road network data comprises:

6. The micro traffic simulation method according to claim 1, wherein the running of the simulation software to perform simulation based on the adapted road network data, the directed sub-road segment sequence path data and the signal control scheme data comprises:

setting simulation parameters of the simulation software;

7. The micro traffic simulation method according to claim 6, further comprising:

8. The micro traffic simulation method according to any one of claims 1 to 7, wherein the simulation software is SUMO simulation software.

9. A computer device comprising a memory for storing at least one program and a processor for loading the at least one program to perform the microscopic traffic simulation method of any one of claims 1-8.

10. A storage medium having stored therein a processor-executable program, wherein the processor-executable program, when executed by a processor, is configured to perform the microscopic traffic simulation method according to any one of claims 1 to 8.