National road function evaluation method and system based on mobile big data, and storage medium

Abstract

The invention provides a national road function evaluation method and system based on mobile big data and a storage medium, and the method comprises the steps: collecting mobile equipment data of a geographic coordinate range of a target national road needing to be evaluated, and carrying out the preprocessing of the collected mobile equipment data; constructing a road network model of a surrounding area of the target national road based on the preprocessed mobile device data; constructing a driving travel demand model based on trajectory data in the mobile device data, and allocating the driving travel demand model to a road network model; and calculating to obtain a traffic operation index and an energy efficiency index of the target national road through the road network model obtained by executing the step S2. According to the method provided by the invention, cross-province-city and cross-basin traffic data acquisition can be realized through mobile equipment data, the method does not need to depend on the installation of field equipment, and the national road operation condition of 7 days * 24 hours can be dynamically reflected in combination with mobile equipment big data. And on the basis, a dynamic evaluation model is provided by combining data changes of population vitality, facility construction and the like around the national road.

Description

Technical Field

[0001] The present invention relates to the field of intelligent travel technology, and in particular to a national highway function evaluation method, system and storage medium based on mobile big data. Background Art

[0002] The national highway network consists of the national expressway network and the ordinary national highway network. Ordinary national highways are the main framework of the national ordinary trunk highway network and play the role of trunk lines in the highway network. However, the traffic function evaluation of the road network covering ordinary national highways has the following difficulties: 1) Evaluation data is difficult to collect: Since national highways generally run through multiple provinces and cities, their traffic flows include local traffic and transit traffic, and national highways are generally not equipped with toll data collection equipment such as ETC, so data collection for post-evaluation of national highways has always been a difficult problem; 2) Dynamic laws are difficult to grasp: The operation of national highways changes dynamically with time and space (economy, population), and traditional static assessment methods are difficult to reflect their long-term and dynamic impacts. Summary of the invention

[0003] The embodiments of the present invention provide a national highway function evaluation method, system and storage medium based on mobile big data, which are used to solve the problems existing in the prior art.

[0004] In order to achieve the above object, the present invention adopts the following technical scheme.

[0005] The national highway function evaluation method based on mobile big data includes: S1 collects mobile device data within the geographic coordinate range of the target national highway to be evaluated, and pre-processes the collected mobile device data; the mobile device data within the geographic coordinate range of the target national highway to be evaluated includes speed data and track point data of the vehicle to which the mobile device belongs; S2 builds a road network model of the surrounding area of ​​the target national highway by processing the nodes and line segments in the trajectory point data based on the preprocessed mobile device data; builds a driving travel demand model by establishing the travel demand OD between travel communities based on the mobile device data, and allocates it to the road network model; S3 calculates and obtains the traffic operation index and energy efficiency index of the target national highway by executing the road network model obtained in step S2; Traffic operation indicators include vehicle speed, traffic saturation and average vehicle delay time; energy efficiency indicators include traffic share, local travel service ratio and road network delay cost; Traffic operation indicators and energy efficiency indicators are used for vehicle travel navigation and target national highway traffic condition assessment.

[0006] Preferably, in step S2, the process of constructing a road network model of the surrounding area of ​​the target national highway includes: S21 projects the vehicle driving trajectory within the geographic coordinate range of the target national highway onto a plane to form a trajectory segment and a trajectory node; connects two ends of the trajectory segment to a single trajectory node, and each trajectory node is respectively connected to two ends of multiple trajectory segments; S22: merge the trajectory line segments with the same starting node and ending node, and merge the trajectory line segments with the azimuth angle formed by the starting node and the ending node less than a first preset threshold and the distance between the starting node and the ending node less than a second preset threshold; S23: deleting the trajectory nodes that only connect two trajectory segments, and merging the trajectory nodes that are less than a third preset threshold and are connected to each other at a short distance; S24: combining the trajectory segments and trajectory nodes obtained by executing sub-step S22 to obtain a regional road network model; In step S2, the process of constructing the driving travel demand model includes: S25, identifying and obtaining a driving behavior trajectory of the vehicle to which the mobile device belongs according to the trajectory path and moving speed of the vehicle to which the mobile device belongs; S26: taking the starting point of the driving behavior trajectory of the vehicle to which the mobile device belongs as the travel starting point, and the stopping point as the travel end point, and projecting the travel starting point and the travel end point of the vehicle to which the mobile device belongs on a plane; S27: Repeat sub-steps S25 and S26 to project the travel start points and travel end points of the vehicles to which all the mobile devices belong on the plane; S28: combining nodes whose relative distances between the travel starting point and the travel end point are less than a fourth preset threshold to obtain a travel community; S29 takes the travel area as a unit, traverses the trajectory lines of driving behaviors in all travel areas, merges the travel starting points and end points between the travel areas, and forms the travel demand OD between the travel areas.

[0007] Preferably, step S3 comprises: S31 distributes the travel demand OD obtained by executing sub-step S29 to the road network model to obtain the cross-sectional flow of each section of the target national highway; S32: screening and obtaining the trajectory points of each section of the target national highway, extracting the vehicle speeds of the obtained trajectory points, and calculating and obtaining the real-time average vehicle speed of each section of the target national highway; S33, during a time period when the flow rate of a certain section of the target national highway is the minimum, obtaining an average vehicle speed of each section of the certain section, and setting the average vehicle speed as the free flow speed; S34 classifies the road sections and counts the traffic flow of each category of roads. Traffic flow share of ordinary national highways = V(ordinary national highways) / (V(highway) + V(ordinary national highways) + V(local roads)) Calculate and obtain the traffic flow share of the target national highway; S35 performs trajectory tracing on the traffic flow passing through the target national highway, obtains the travel area where the vehicle travel start point is located and the travel area where the vehicle travel end point is located, and according to the positional relationship between the travel area and the surrounding area of ​​the target national highway, the formula Proportion of local travel services = V (oZone ∈ localArea or dZone ∈ localArea) / V (total) Proportion of local travel services obtained; S36 In the road network model obtained by executing step S2, the area where the target national highway is located and the area without the target national highway are modeled separately, and the following equations are used to respectively Road network delay cost = ∑Di (无普通国道) (i∉ordinary national highway) - ∑Di (有普通国道) (i∉Ordinary national highway) Section flow change = Vi (无普通国道) (i∉ordinary national highway) - Vi (有普通国道) (i∉Ordinary national highway) Section speed change = Si (无普通国道) (i∉ordinary national highway) - Si (有普通国道) (i∉Ordinary national highway) The traffic operation index and energy efficiency index of the target national highway are calculated; where i represents road section i, D represents delay time, V represents flow rate, and S represents speed.

[0008] In a second aspect, the present invention provides a national highway traffic function evaluation system based on mobile device big data, comprising: The data collection and processing module is used to collect the mobile device data within the geographical coordinate range of the target national highway to be evaluated, and pre-process the collected mobile device data; the mobile device data within the geographical coordinate range of the target national highway to be evaluated includes the speed data and track point data of the vehicle to which the mobile device belongs; The analysis and evaluation module is used to: construct a road network model of the surrounding area of ​​the target national highway by processing the nodes and line segments in the trajectory point data based on the preprocessed mobile device data; construct a driving travel demand model by establishing the travel demand OD between travel communities based on the mobile device data, and allocate it to the road network model; By executing the road network model obtained through the above process, the traffic operation index and energy efficiency index of the target national highway are calculated; Traffic operation indicators include vehicle speed, traffic saturation and average vehicle delay time; energy efficiency indicators include traffic share, local travel service ratio and road network delay cost.

[0009] In a third aspect, the present invention provides a computer-readable storage medium storing a program capable of executing the above method.

[0010] It can be seen from the technical solutions provided by the embodiments of the present invention that the present invention provides a method, system and storage medium for evaluating the function of national highways based on mobile big data, wherein the method includes: collecting mobile device data within the geographical coordinate range of the target national highway to be evaluated, and preprocessing the collected mobile device data; constructing a road network model of the surrounding area of ​​the target national highway based on the preprocessed mobile device data; constructing a driving travel demand model based on the trajectory data in the mobile device data, and assigning it to the road network model; calculating and obtaining the traffic operation index and energy efficiency index of the target national highway by executing the road network model obtained in step S2. The method provided by the present invention can realize the collection of cross-provincial and cross-basin traffic data through mobile device data, and does not need to rely on the installation of on-site equipment. Combined with mobile device big data, it can dynamically reflect the operation of national highways for 7 days x 24 hours, including the operation of national highways on holidays. On this basis, combined with the changes in data such as population vitality and facility construction around the national highway, a set of dynamic evaluation models has been developed.

[0011] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0013] Figure 1 A processing flow chart of the national highway traffic function evaluation method based on mobile device big data provided by the present invention; Figure 2 A schematic diagram of a road network model of a national highway traffic function evaluation method based on mobile device big data provided by the present invention; Figure 3 A schematic diagram of the execution process of a preferred embodiment of the national highway traffic function evaluation method based on mobile device big data provided by the present invention; Figure 4 This is a logical block diagram of the national highway traffic function evaluation system based on mobile device big data provided by the present invention.

[0014] In the figure: 401. Data acquisition and processing module 402. Analysis and evaluation module. DETAILED DESCRIPTION

[0015] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0016] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0017] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0018] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0019] See also Figure 1 The present invention provides a method for evaluating national highway traffic function based on mobile device big data, comprising the following steps: S1 collects mobile device data within the geographic coordinate range of the target national highway to be evaluated, and pre-processes the collected mobile device data; the mobile device data within the geographic coordinate range of the target national highway to be evaluated includes speed data and track point data of the vehicle to which the mobile device belongs; S2 constructs a road network model of the surrounding area of ​​the target national highway by processing the nodes and segments in the trajectory point data based on the preprocessed mobile device data; constructs a driving travel demand model by establishing the travel demand OD between travel communities based on the trajectory data in the mobile device data, and allocates it to the road network model; S3 calculates the traffic operation index and energy efficiency index of the target national highway by executing the road network model obtained in step S2. The traffic operation index of the national highway includes: vehicle speed, flow saturation and average vehicle delay time. The energy efficiency index of the national highway includes: traffic sharing, local travel service ratio, road network delay cost, etc.

[0020] The above traffic operation indicators and energy efficiency indicators are used for vehicle travel navigation and target national highway traffic condition assessment. For example, an indicator model is constructed based on the above traffic operation indicators and energy efficiency indicators, and the quantification of the above indicators is integrated. In some feasible embodiments, the process of preprocessing data in step S1 includes: According to the characteristics of mobile device data, including mobile device speed, mobile device trajectory points, etc., the driving mobile device data is extracted. On this basis, the traffic flow of each section of the national highway is sorted out, the driving trajectory of the driving mobile device is restored to a trajectory line, and the driving speed data of each section is counted.

[0021] In a preferred embodiment provided by the present invention, in step S2, the process of constructing a road network model of the surrounding area of ​​the target national highway includes: S21 projects the vehicle driving trajectory within the geographic coordinate range of the target national highway onto a plane to form a trajectory segment and a trajectory node; connects two ends of the trajectory segment to a single trajectory node, and each trajectory node is respectively connected to two ends of multiple trajectory segments; S22 merges the trajectory line segments with the same starting node and ending node, and merges the trajectory line segments with the starting node and the ending node having a close azimuth angle and a close distance between the starting node and the ending node; The above-mentioned "close" judgment can be achieved by setting a preset threshold. For example, the above-mentioned sub-steps can be specifically: merging the trajectory segments whose azimuth angle formed by the starting node and the ending node is less than the first preset threshold and whose distance between the starting node and the ending node is less than the second preset threshold; the following sub-steps are similar.

[0022] S23: deleting the trajectory nodes that only connect two trajectory segments, and merging the trajectory nodes that are close in distance (less than a third preset threshold) and are connected to each other at a short distance; S24: combining the trajectory segments and trajectory nodes obtained by executing sub-step S22 to obtain the regional road network model; In step S2, the process of constructing the driving travel demand model includes: S25, identifying and obtaining a driving behavior trajectory of the vehicle to which the mobile device belongs according to the trajectory path and moving speed of the vehicle to which the mobile device belongs; S26: taking the starting point of the driving behavior trajectory of the vehicle to which the mobile device belongs as the travel starting point, and the stopping point as the travel end point, and projecting the travel starting point and the travel end point of the vehicle to which the mobile device belongs on a plane; S27: Repeat sub-steps S25 and S26 to project the travel start points and travel end points of the vehicles to which all the mobile devices belong on the plane; S28: combining nodes whose relative distances between the travel start point and the travel end point are relatively close (less than a fourth preset threshold) to obtain a travel community; S29 takes the travel area as a unit, traverses the trajectory lines of driving behaviors in all travel areas, merges the travel starting points and end points between the travel areas, and forms the travel demand OD between the travel areas.

[0023] Step S3 includes the following execution process: S31 distributes the travel demand OD obtained by executing sub-step S29 to the road network model to obtain the cross-sectional flow of each section of the target national highway; S32: screening and obtaining the trajectory points of each section of the target national highway, extracting the vehicle speeds of the obtained trajectory points, and calculating and obtaining the real-time average vehicle speed of each section of the target national highway; S33, during a time period when the flow rate of a certain section of the target national highway is the minimum, obtaining an average vehicle speed of each section of the certain section, and setting it as a free flow speed; this sub-step is executed multiple times to obtain all free flow speeds; S34 classifies the road sections and counts the traffic flow of each category of roads. Traffic flow share of ordinary national highways = V(ordinary national highways) / (V(highway) + V(ordinary national highways) + V(local roads)) Calculate the traffic flow share of the target national highway; the road sections are classified into expressways, ordinary national highways, and local roads (main roads, secondary roads, and branch roads); S35 performs trajectory tracing on the traffic flow passing through the target national highway, obtains the travel area where the vehicle travel starts and the travel area where the vehicle travel ends, and according to the positional relationship between the travel area and the surrounding area of ​​the target national highway, the formula Proportion of local travel services = V (oZone ∈ localArea or dZone ∈ localArea) / V (total) Proportion of local travel services obtained; S36 In the road network model obtained by executing step S2, the area where the target national highway is located and the area without the target national highway are modeled separately, and the following equations are used to respectively describe the road network model: Road network delay cost = ∑Di (无普通国道) (i∉ordinary national highway) - ∑Di (有普通国道) (i∉Ordinary national highway) Section flow change = Vi (无普通国道) (i∉ordinary national highway) - Vi (有普通国道) (i∉Ordinary national highway) Section speed change = Si (无普通国道) (i∉ordinary national highway) - Si (有普通国道) (i∉Ordinary national highway) The traffic operation index and energy efficiency index of the target national highway are calculated; where i represents road section i, D represents delay time, V represents flow rate, and S represents speed. In a second aspect, the present invention provides a national highway traffic function evaluation system based on mobile device big data, comprising: The data collection and processing module 401 is used to collect mobile device data within the geographical coordinate range of the target national highway to be evaluated, and pre-process the collected mobile device data; The analysis and evaluation module 402 is used to: construct a road network model of the surrounding area of ​​the target national highway based on the pre-processed mobile device data; construct a driving travel demand model based on the trajectory data in the mobile device data, and allocate it to the road network model; By executing the road network model obtained through the above process, the traffic operation index and energy efficiency index of the target national highway are calculated. In a third aspect, the present invention provides a computer-readable storage medium storing a program capable of executing the above method. In summary, the present invention provides a method, system and storage medium for evaluating the function of national highways based on mobile big data, wherein the method includes: collecting mobile device data within the geographic coordinate range of the target national highway to be evaluated, and preprocessing the collected mobile device data; constructing a road network model of the surrounding area of ​​the target national highway based on the preprocessed mobile device data; constructing a driving travel demand model based on the trajectory data in the mobile device data, and assigning it to the road network model; calculating and obtaining the traffic operation index and energy efficiency index of the target national highway by executing the road network model obtained in step S2. The method provided by the present invention can realize the collection of cross-provincial and cross-basin traffic data through mobile device data, and does not need to rely on the installation of on-site equipment. Combined with mobile device big data, it can dynamically reflect the operation of national highways for 7 days x 24 hours, including the operation of national highways on holidays. And on this basis, combined with the changes in data such as population vitality and facility construction around the national highway, a set of dynamic evaluation models has been developed. Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0024] It can be known from the above description of the implementation mode that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention or certain parts of the embodiments.

[0025] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, in which the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0026] The above are only preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. The national highway function evaluation method based on mobile big data is characterized by: include: S1 collects mobile device data within the geographic coordinate range of the target national highway to be evaluated, and pre-processes the collected mobile device data; The mobile device data within the geographical coordinate range of the target national highway to be evaluated includes speed data and track point data of the vehicle to which the mobile device belongs; S2 constructs a road network model of the surrounding area of ​​the target national highway by processing the nodes and line segments in the trajectory point data based on the preprocessed mobile device data; constructs a driving travel demand model by establishing the travel demand OD between travel communities based on the mobile device data, and allocates it to the road network model; S3 calculates and obtains the traffic operation index and energy efficiency index of the target national highway by executing the road network model obtained in step S2; Traffic operation indicators include vehicle speed, traffic saturation and average vehicle delay time; energy efficiency indicators include traffic share, local travel service ratio and road network delay cost; The traffic operation index and energy efficiency index are used for vehicle travel navigation and target national highway traffic condition assessment.

2. The method according to claim 1, characterized in that In step S2, the process of constructing a road network model of the surrounding area of ​​the target national highway includes: S21 projects the vehicle driving trajectory within the geographic coordinate range of the target national highway onto a plane to form a trajectory segment and a trajectory node; connects two ends of the trajectory segment to a single trajectory node, and each trajectory node is respectively connected to two ends of multiple trajectory segments; S22 merges the trajectory line segments with the same starting node and ending node, and merges the trajectory line segments with the azimuth angle formed by the starting node and the ending node being less than a first preset threshold and the distance between the starting node and the ending node being less than a second preset threshold; S23: deleting the trajectory nodes that only connect two trajectory segments, and merging the trajectory nodes that are less than a third preset threshold and are connected to each other at a short distance; S24: combining the trajectory segments and trajectory nodes obtained by executing sub-step S22 to obtain the regional road network model; In step S2, the process of constructing the driving travel demand model includes: S25, according to the trajectory path and moving speed of the vehicle to which the mobile device belongs, identifying and obtaining the trajectory of the driving behavior of the vehicle to which the mobile device belongs; S26: taking the starting point of the driving behavior trajectory of the vehicle to which the mobile device belongs as the travel starting point, and the stopping point as the travel end point, and projecting the travel starting point and the travel end point of the vehicle to which the mobile device belongs on a plane; S27: Repeat sub-steps S25 and S26 to project the travel start points and travel end points of the vehicles to which all the mobile devices belong on the plane; S28: combining nodes whose relative distances between the travel starting point and the travel end point are less than a fourth preset threshold to obtain a travel community; S29 takes the travel area as a unit, traverses the trajectory lines of driving behaviors in all travel areas, merges the travel starting points and end points between the travel areas, and forms the travel demand OD between the travel areas.

3. The method according to claim 2, characterized in that Step S3 includes: S31 distributes the travel demand OD obtained by executing sub-step S29 to the road network model to obtain the cross-sectional flow of each section of the target national highway; S32: screening and obtaining the trajectory points of each section of the target national highway, extracting the vehicle speeds of the obtained trajectory points, and calculating and obtaining the real-time average vehicle speed of each section of the target national highway; S33, during a time period when the flow rate of a certain section of the target national highway is the minimum, obtaining an average vehicle speed of each section of the certain section, and setting the average vehicle speed as the free flow speed; S34 classifies the road sections and counts the traffic flow of each category of roads. Traffic flow share of ordinary national highways = V(ordinary national highways) / (V(highway) + V(ordinary national highways) + V(local roads)) Calculate and obtain the traffic flow share of the target national highway; S35 performs trajectory tracing on the traffic flow passing through the target national highway, obtains the travel area where the vehicle travel start point is located and the travel area where the vehicle travel end point is located, and according to the positional relationship between the travel area and the surrounding area of ​​the target national highway, the formula Proportion of local travel services = V (oZone ∈ localArea or dZone ∈ localArea) / V (total) Proportion of local travel services obtained; S36 In the road network model obtained by executing step S2, the area where the target national highway is located and the area without the target national highway are modeled separately, and the following equations are used to respectively describe the road network model: Road network delay cost = ∑Di (无普通国道) (i∉ordinary national highway) - ∑Di (有普通国道) (i∉Ordinary national highway) Section flow change = Vi (无普通国道) (i∉ordinary national highway) - Vi (有普通国道) (i∉Ordinary national highway) Section speed change = Si (无普通国道) (i∉ordinary national highway) - Si (有普通国道) (i∉Ordinary national highway) The traffic operation index and energy efficiency index of the target national highway are calculated; where i represents road section i, D represents delay time, V represents flow rate, and S represents speed.

4. The national highway traffic function evaluation system based on mobile device big data is characterized by: include: A data collection and processing module, used to collect mobile device data within the geographical coordinate range of the target national highway to be evaluated, and pre-process the collected mobile device data; the mobile device data within the geographical coordinate range of the target national highway to be evaluated includes speed data and track point data of the vehicle to which the mobile device belongs; The analysis and evaluation module is used to: construct a road network model of the surrounding area of ​​the target national highway by processing the nodes and line segments in the trajectory point data based on the preprocessed mobile device data; construct a driving travel demand model by establishing the travel demand OD between travel communities based on the mobile device data, and allocate it to the road network model; By executing the road network model obtained by the above process, the traffic operation index and energy efficiency index of the target national highway are calculated; Traffic operation indicators include vehicle speed, traffic saturation and average vehicle delay time; energy efficiency indicators include traffic share, local travel service ratio and road network delay cost.

5. A computer-readable storage medium, characterized in that: A program is stored, and the program can execute the method according to any one of claims 1 to 3.

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