CN114863684B - Urban traffic network facility feature analysis system - Google Patents
Urban traffic network facility feature analysis system Download PDFInfo
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- CN114863684B CN114863684B CN202210637027.6A CN202210637027A CN114863684B CN 114863684 B CN114863684 B CN 114863684B CN 202210637027 A CN202210637027 A CN 202210637027A CN 114863684 B CN114863684 B CN 114863684B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention relates to a city traffic network facility characteristic analysis system, which belongs to the traffic planning field, and comprises: the road network characteristic analysis module is used for acquiring road network characteristic indexes in the target area; the public transport network characteristic analysis module is used for acquiring public transport network characteristic indexes in the target area; the track network characteristic analysis module is used for acquiring a track network static characteristic index in the target area; the slow-going network characteristic analysis module is used for acquiring slow-going network characteristic indexes in the target area; the data statistics analysis module is used for displaying the data of the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow-going network feature analysis module in the self-defined analysis granularity area in the self-defined time period.
Description
Technical Field
The invention relates to the technical field of urban traffic planning, in particular to a characteristic analysis system of urban traffic network facilities.
Background
Along with the development of time, china needs to build a modern high-quality national comprehensive three-dimensional traffic network which is convenient, quick, smooth, economical, efficient, green, intensive, intelligent, advanced, safe and reliable. The important component in the comprehensive three-dimensional traffic network is the construction of urban traffic network facilities. With the growth of urban population and urban financial resources, urban traffic network facilities provide safety guarantee for daily travel of residents and meet convenience requirements, and the requirements are increasingly strong, so that the comprehensive analysis of various aspects of facility supply, travel service, mode selection, policy management and the like of the urban traffic network facilities is also increasingly necessary, and the urban traffic network facilities mainly comprise four traffic network facilities including road network, public transport network, rail network and slow-going network.
The existing researches have the following defects: firstly, the existing researches are mainly characterized by only carrying out feature analysis from certain indexes of single traffic network facilities, wherein the feature data is single in form, the parallel analysis of the traffic network facilities in a cross form cannot be realized, and the analysis is limited; and secondly, the traditional traffic network facility operation research has the advantages of short data period, low refinement degree, small data range and low granularity in space.
Disclosure of Invention
The invention aims to provide an urban traffic network facility characteristic analysis system which can more comprehensively obtain characteristic data of four traffic network facilities, namely a road network, a public transportation network, a track network and a slow-running network, has the characteristics of long period and high refinement degree, has the characteristics of large range and high granularity in space, and improves the accuracy of obtaining the running condition of the traffic network.
In order to achieve the above object, the present invention provides the following solutions:
an urban traffic network facility feature analysis system, comprising:
the road network characteristic analysis module is used for acquiring road network characteristic indexes in a target area, wherein the road network characteristic indexes comprise road network static characteristic indexes and road network dynamic characteristic indexes, the road network static characteristic indexes comprise road network connectivity, broken road number, road network density, intersection distance and intersection level difference, and the road network dynamic characteristic indexes comprise traffic running indexes, running average speed, running minimum speed and congestion duration;
the bus network characteristic analysis module is used for acquiring bus network characteristic indexes in a target area, wherein the bus network characteristic indexes comprise bus network static characteristic indexes and bus network dynamic characteristic indexes, the bus network static characteristic indexes comprise bus line mileage, bus network density, bus station spacing and bus station number, and the bus network dynamic characteristic indexes comprise bus average running speed and bus speed difference;
the track network characteristic analysis module is used for acquiring track network static characteristic indexes in the target area, wherein the track network static characteristic indexes comprise track mileage, track line density, track station spacing and track station number;
the slow network characteristic analysis module is used for acquiring slow network characteristic indexes in a target area, wherein the slow network characteristic indexes comprise slow network static characteristic indexes and slow network dynamic characteristic indexes, the slow network static characteristic indexes comprise bicycle special road mileage, bicycle service site quantity and bicycle parking space quantity, and the slow network dynamic characteristic indexes comprise bicycle quantity;
the data statistics analysis module is respectively connected with the road network characteristic analysis module, the public transportation network characteristic analysis module, the track network characteristic analysis module and the slow-running network characteristic analysis module; the data statistics analysis module comprises a first static index data display unit, a first dynamic index data display unit, a second static index data display unit, a second dynamic index data display unit, a third static index data display unit, a fourth static index data display unit and a third dynamic index data display unit;
the first static index data display unit is used for displaying the static characteristic index of the road network in the self-defined analysis granularity area in the self-defined time period, and the first dynamic index data display unit is used for displaying the dynamic characteristic index of the road network in the self-defined analysis granularity area in the self-defined time period;
the second static index data display unit is used for displaying the static characteristic index of the public transportation network in the self-defined analysis granularity area in the self-defined time period, and the second dynamic index data display unit is used for displaying the dynamic characteristic index of the public transportation network in the self-defined analysis granularity area in the self-defined time period;
the third static index data display unit is used for displaying the static characteristic index of the track network in the self-defined analysis granularity area in the self-defined time period;
the fourth static index data display unit is used for displaying the static characteristic index of the slow network in the self-defined analysis granularity area in the self-defined time period, and the third dynamic index data display unit is used for displaying the dynamic characteristic index of the slow network in the self-defined analysis granularity area in the self-defined time period.
Optionally, the method further comprises:
the road network characteristic evaluation module is used for determining the entropy weight of each road network characteristic index in the target area by adopting an entropy weight method, grading each road network characteristic index by adopting a fuzzy comprehensive evaluation method, multiplying the corresponding entropy weight by the level of each road network characteristic index and summing the multiplied corresponding entropy weight to obtain an evaluation result of road network facilities;
the public transport network characteristic evaluation module is used for determining the entropy weight of each public transport network characteristic index in the target area by adopting an entropy weight method, classifying each public transport network characteristic index by adopting a fuzzy comprehensive evaluation method, multiplying the level of each public transport network characteristic index by the corresponding entropy weight and summing up to obtain an evaluation result of public transport network facilities;
the track network characteristic evaluation module is used for determining the entropy weight of each track network static characteristic index in the target area by adopting an entropy weight method, classifying each track network static characteristic index by adopting a fuzzy comprehensive evaluation method, multiplying the corresponding entropy weight by the level of each track network static characteristic index and summing the corresponding entropy weight to obtain an evaluation result of track network facilities;
the slow network characteristic evaluation module is used for determining the entropy weight of each slow network characteristic index in the target area by adopting an entropy weight method, classifying each slow network characteristic index by adopting a fuzzy comprehensive evaluation method, multiplying the corresponding entropy weight by the level of each slow network characteristic index and summing the corresponding entropy weight to obtain the evaluation result of the slow network facility.
Optionally, the system further comprises a data storage module for storing the data acquired by the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow network feature analysis module.
Optionally, the system further comprises a time self-defining module, wherein the time self-defining module is used for self-defining a time period for acquiring data by the road network characteristic analysis module, the public transportation network characteristic analysis module, the track network characteristic analysis module and the slow network characteristic analysis module, and the self-defining time period comprises a self-defining date and a self-defining time period; the custom date includes year, quarter, working day and non-working day, and the custom time period includes early peak time period, late peak time period and minor period.
Optionally, the system further comprises a granularity selection module, which is respectively connected with the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow network feature analysis module and is used for supporting the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow network feature analysis module to acquire data from specified space granularity, wherein the specified space granularity comprises a county level, a street level, a traffic cell and a grid level.
Optionally, the granularity selection module is connected with the data statistics analysis module and is used for displaying data from the specified spatial granularity by the first static index data display unit, the first dynamic index data display unit, the second static index data display unit, the second dynamic index data display unit, the third static index data display unit, the fourth static index data display unit and the third dynamic index data display unit.
Optionally, the system further comprises a data export module, which is respectively connected with the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow network feature analysis module and is used for exporting the data acquired by the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow network feature analysis module with specified space granularity in a custom time period;
optionally, the data exported by the data export module is displayed in a data table form, a data change curve form, a picture form or a map form.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses an urban traffic network facility characteristic analysis system, which is used for respectively obtaining characteristic data from road network characteristics, bus network characteristics, track network characteristics and slow-running network characteristics, has the characteristics of long period and high refinement degree in time, has the characteristics of large range and high granularity in space, can be used for more comprehensively obtaining traffic network facility characteristic data, improves the accuracy of obtaining traffic network running conditions, and provides more reliable basis for traffic network facility construction.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system for analyzing characteristics of urban traffic network facilities;
FIG. 2 is a road network characteristic evaluation index system according to the present invention;
FIG. 3 is a system of characteristic evaluation indicators of the public transportation network according to the present invention;
FIG. 4 is a graphical representation of an exemplary system for evaluating the characteristics of a rail network in accordance with the present invention;
FIG. 5 is a slow-going network characteristic evaluation index system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide the urban traffic network facility feature analysis system, which can more comprehensively acquire urban traffic network facility feature data, find out characteristic bases and differences of the urban traffic network facilities, guide development directions, improve the accuracy of acquiring traffic network running conditions, provide more reliable basis for improving urban traffic network infrastructure construction, provide direction guidance for improving urban traffic travel service quality, and provide technical support for urban traffic policy formulation and real-time management.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a schematic structural diagram of an urban traffic network facility feature analysis system according to the present invention, as shown in fig. 1, an urban traffic network facility feature analysis system includes:
the road network characteristic analysis module is used for acquiring road network characteristic indexes in a target area, wherein the road network characteristic indexes comprise road network static characteristic indexes and road network dynamic characteristic indexes, the road network static characteristic indexes comprise road network connectivity, broken road number, road network density, intersection distance and intersection level difference, and the road network dynamic characteristic indexes comprise traffic running indexes, running average speed, running minimum speed and congestion duration.
The road network characteristic analysis module supports displaying real-time road conditions and urban road construction conditions, and is beneficial to making and adjusting traffic planning and management schemes.
The road network connectivity reflects the accessibility of the road network and is one of indexes for checking the structural rationality of the road network. The higher the connectivity is, the better the road network connectivity is; conversely, the lower the connectivity, the worse the road network connectivity. The connectivity of urban road network is closely related to urban scale and urban morphology. The road network connectivity is calculated based on the road network acquired road section data and the node data stored in the data storage module, and the calculation mode is as follows: the ratio of the total number of adjacent road segments to the total number of nodes.
The broken road number is the number of roads which are not connected with other forming road networks in the area. And acquiring road section data based on the urban road network stored in the data storage module, judging that one end of the road is not connected with the road sections of other formed road networks, and calculating the total number as the number of broken roads.
The road network density is the mileage number of the road network in the unit area of the reflection area, and is one of basic indexes for evaluating whether the urban road network is reasonable. Road network density is calculated based on the urban road network acquired road section data stored in the data storage module and the urban area acquired area data stored in the data storage module, wherein the calculation mode is the ratio of the total mileage of the road network in the target area to the area of the area.
The intersection distance refers to the distance between the center points of two adjacent road intersections, and is one of basic indexes for evaluating whether urban intersection construction is reasonable or not. Too large intersection distance and inconvenient traffic connection; the distance is too small and the speed of the vehicle running cannot be increased. And calculating the intersection distance based on the road section data and the intersection data acquired by the urban road network stored in the data storage module in a way of calculating the distance between the center points of two adjacent roads.
The intersection level difference is used for dividing the urban road into eight levels according to the number of lanes, is a level difference value between two roads connected with the intersection, reflects the functional matching degree of the intersection road, and is one of indexes for checking the structural rationality of the intersection. And calculating the intersection level difference based on the urban road network acquired road section data and the intersection data stored in the data storage module in a way of calculating the difference of the total number of the import and export lanes between two roads connected with the intersection.
Traffic running index refers to a conceptual index value that comprehensively reflects the smoothness or congestion of a road network. And calculating a traffic running index based on the road travel acquired travel time data stored in the data storage module, wherein the calculation mode is the ratio of the actual travel time consumption to the travel time consumption under the free flow condition.
The running average speed is the regional road traffic flow average speed. And calculating running average speed based on the road travel acquired travel speed data stored in the data storage module in a mode of calculating the average value of the speeds of all road traffic flows in the target area.
The bus network characteristic analysis module is used for acquiring bus network characteristic indexes in a target area, wherein the bus network characteristic indexes comprise bus network static characteristic indexes and bus network dynamic characteristic indexes, the bus network static characteristic indexes comprise bus line mileage, bus network density, bus station spacing and bus station number, and the bus network dynamic characteristic indexes comprise bus average running speed and bus speed difference.
The bus network characteristic analysis module can intuitively display a plurality of bus lines and bus running indexes, accurately indicates short boards of bus running efficiency, and has great reference significance for making bus priority policies.
The mileage of the public transportation line is the total mileage of the public transportation line in the area. And acquiring bus mileage data based on a bus network stored in the data storage module, wherein the calculation mode is to sum all bus route mileage in the target area.
Bus stop distance refers to the distance between two adjacent bus stops. And calculating the distance between bus stops based on the bus network stored in the data storage module and the bus route data, wherein the calculating mode is to calculate the distance between two adjacent bus stops according to the bus route.
The bus network density is the ratio of the total mileage of the bus route in the target area to the area of the target area, and is one of basic indexes for evaluating whether the urban bus route planning is reasonable. And obtaining bus mileage data based on a bus network stored in a data storage module, and obtaining target area data of a city area stored in the data storage module to calculate bus network density in a mode of calculating the ratio of the total mileage of a bus line in the target area to the target area.
The number of bus stops refers to the number of bus stops in a target area, and is one of basic indexes for evaluating whether urban bus route planning is reasonable or not. And acquiring bus stop data based on a bus network stored in the data storage module, wherein the number of bus stops is calculated by adding the number of bus stops in the target area.
The average running speed of buses refers to the average running speed of all public transportation tools in a target area, and is one of basic indexes for evaluating whether urban buses run smoothly or not. And acquiring bus running speed data based on a bus network stored in the data storage module, wherein the calculation mode of the bus average running speed is to calculate the average running speed of all public transportation means in the target area.
The bus speed difference refers to the difference between the average running speeds of all buses and buses in the target area. The method comprises the steps of obtaining bus running speed data based on a bus network stored in a data storage module, obtaining bus running speed data by an urban road network stored in the data storage module, calculating bus speed difference in a mode of calculating average running speeds of all public transportation in a target area and making a difference value with the average running speeds of buses, wherein the buses are buses in a preset vehicle type range, and the buses comprise cars, SUVs (sport utility vehicles) and MPVs (utility vehicles).
The track network characteristic analysis module is used for acquiring track network static characteristic indexes in the target area in real time, wherein the track network static characteristic indexes comprise track mileage, track station spacing, track line density and track station number.
The track network characteristic analysis module can intuitively display track traffic facilities, analyze the layout and operation conditions of the track traffic facilities and provide guidance comments for network planning and policy formulation of track traffic.
The track mileage represents the total mileage of the track traffic route in the target area. Track mileage data is acquired based on the track network stored in the data storage module, and the calculation mode is to sum all track line mileage in the target area.
Rail station pitch means the distance between two adjacent rail stations. And acquiring track site and track route data based on the track network stored in the data storage module, wherein the calculation mode is to calculate the distance between two adjacent track sites according to the track route.
The track line density represents the ratio of the total mileage of the track line in the target area to the area of the target area, and is one of basic indexes for evaluating whether urban track line planning is reasonable. Track mileage data is acquired based on a track network stored in a data storage module, track line density is calculated based on urban area acquisition area data stored in the data storage module, and the calculation mode is the ratio of the total mileage of a track line in a target area to the area of the target area.
The number of the track stations represents the number of the track stations in the target area, and is one of basic indexes for evaluating whether urban track line planning is reasonable or not. Track site data is acquired based on a track network stored in a data storage module, and the track site data is calculated by adding the number of track sites in a target area.
The slow-running network characteristic analysis module is used for acquiring slow-running network characteristic indexes in a target area, wherein the slow-running network characteristic indexes comprise slow-running network static characteristic indexes and slow-running network dynamic characteristic indexes, the slow-running network static characteristic indexes comprise bicycle specific road mileage, bicycle service site quantity and bicycle parking space quantity, and the slow-running network dynamic characteristic indexes comprise bicycle quantity.
The slow-going network characteristic analysis module intuitively displays the slow-going traffic infrastructure and the running condition of the bicycle, and has reference significance for perfecting the slow-going traffic infrastructure, improving the slow-going traffic service quality and formulating the slow-going traffic related policy.
The number of bicycle service stations is the number of bicycle service stations in the target area, and is one of the basic indexes for evaluating the perfection of urban slow traffic infrastructure. The bicycle service site data is acquired based on the slow traffic stored in the data storage module, and the calculation mode is that the number of bicycle service sites in the target area is added.
The mileage of the bicycle lane is the total mileage of the bicycle lane in the target area, and is one of the basic indexes for evaluating the urban slow traffic infrastructure environment. The bicycle specific road mileage data is obtained based on the slow traffic stored in the data storage module in such a way that the total mileage of the bicycle specific road in the target area is added.
The number of bicycles is the number of bicycles driven in the target area, and is one of the basic indexes for evaluating the service coverage of the city residents. The bicycle number data is acquired based on the slow traffic stored in the data storage module, calculated by adding the bicycle numbers in the target area.
The number of bicycle parking spaces is the number of bicycle parking spaces running in a target area, and is one of basic indexes for evaluating the perfection of urban slow traffic infrastructure. The method comprises the steps of obtaining bicycle parking space quantity data based on the slow traffic stored in the data storage module, wherein the calculation mode is to add the bicycle parking space quantity in the area.
The target areas include county level, street level, traffic cell, and grid level areas.
The urban traffic network facility feature analysis system further comprises a data statistics analysis module which is respectively connected with the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow-running network feature analysis module; the data statistics analysis module comprises a first static index data display unit, a first dynamic index data display unit, a second static index data display unit, a second dynamic index data display unit, a third static index data display unit, a fourth static index data display unit and a third dynamic index data display unit.
The first static index data display unit is used for displaying the static characteristic index of the road network in the self-defined analysis granularity area in the self-defined time period, and the first dynamic index data display unit is used for displaying the dynamic characteristic index of the road network in the self-defined analysis granularity area in the self-defined time period.
The second static index data display unit is used for displaying the static characteristic index of the public transportation network in the self-defined analysis granularity area in the self-defined time period, and the second dynamic index data display unit is used for displaying the dynamic characteristic index of the public transportation network in the self-defined analysis granularity area in the self-defined time period.
The third static index data display unit is used for displaying the static characteristic index of the track network in the self-defined analysis granularity area in the self-defined time period.
The fourth static index data display unit is used for displaying the static characteristic index of the slow network in the self-defined analysis granularity area in the self-defined time period, and the third dynamic index data display unit is used for displaying the dynamic characteristic index of the slow network in the self-defined analysis granularity area in the self-defined time period.
And establishing a road network characteristic evaluation index system according to the road network characteristic indexes, wherein each index in the road network characteristic evaluation index system is divided into road network factors, intersection factors and road network operation factors as shown in fig. 2. And establishing a bus network characteristic evaluation index system according to the bus network characteristic index, wherein the bus network characteristic evaluation index system comprises bus network factors, bus stop factors and bus running factors as shown in fig. 3. And establishing a track network characteristic evaluation index system according to the track network static characteristic indexes, wherein the track network characteristic evaluation index system comprises track network factors and track site factors as shown in fig. 4. And establishing a slow-going network characteristic evaluation index system according to the slow-going network characteristic indexes, wherein the slow-going network characteristic evaluation index system comprises bicycle driving factors and bicycle station factors as shown in fig. 5. The evaluation index systems are divided into three layers, namely a target layer, a first-level index layer and a second-level index layer, wherein the road network characteristic evaluation index system is taken as an example, and the target layer comprises road network factors, intersection factors and road network operation factors; the second-level index layer comprises road network connectivity, broken road number, road network density, intersection distance, intersection level difference, traffic running index, running average speed, running lowest speed and congestion duration.
The road network characteristic evaluation module is used for determining the entropy weight of each road network characteristic index in the target area by adopting an entropy weight method, classifying each road network characteristic index into five grades from weak to strong by adopting a fuzzy comprehensive evaluation method, multiplying the grade of each road network characteristic index by the corresponding entropy weight and summing the grade of each road network characteristic index to obtain an evaluation result of road network facilities. The method specifically comprises the following steps: multiplying the level of each road network characteristic index by the corresponding entropy weight and summing to obtain a first summation result (evaluation result of road network facilities), comparing the first summation value with a first set threshold, judging that the road network facilities in the target area are insufficient if the first summation value is smaller than the first set threshold, thereby realizing quantitative evaluation of the road network facilities, providing more reliable basis for road network facility construction, and providing lifting direction of the road network facilities according to the grades corresponding to the road network characteristic indexes if the road network facilities in the target area are judged to be insufficient, for example, lifting the road network facilities corresponding to the indexes smaller than three levels in the road network characteristic indexes.
The public transport network characteristic evaluation module is used for determining the entropy weight of each public transport network characteristic index in the target area by adopting an entropy weight method, classifying each public transport network characteristic index into five grades from weak to strong by adopting a fuzzy comprehensive evaluation method, multiplying the grade of each public transport network characteristic index by the corresponding entropy weight and summing to obtain an evaluation result of public transport network facilities. The method specifically comprises the following steps: multiplying the level of each public transportation network characteristic index by the corresponding entropy weight and summing to obtain a second summation result (an evaluation result of public transportation network facilities), comparing the second summation value with a second set threshold, judging that the public transportation network facilities in the target area are insufficient if the second summation value is smaller than the second set threshold, thereby realizing quantitative evaluation of the public transportation network facilities, providing more reliable basis for public transportation network facility construction, and providing the lifting direction of the public transportation network facilities according to the grades corresponding to the public transportation network characteristic indexes if the public transportation network facilities in the target area are judged to be insufficient, for example, lifting the public transportation network facilities corresponding to the indexes smaller than three levels in the public transportation network characteristic indexes.
The track network characteristic evaluation module is used for determining the entropy weight of each track network static characteristic index in the target area by adopting an entropy weight method, classifying each track network static characteristic index into five grades from weak to strong by adopting a fuzzy comprehensive evaluation method, multiplying the grade of each track network static characteristic index by the corresponding entropy weight and summing to obtain the evaluation result of the track network facility. The method specifically comprises the following steps: multiplying the level of the static characteristic index of each track network by the corresponding entropy weight and summing to obtain a third summation result (evaluation result of the track network facilities), comparing the third summation value with a third set threshold, judging that the track network facilities in the target area are insufficient if the third summation value is smaller than the third set threshold, thereby realizing quantitative evaluation of the track network facilities, providing more reliable basis for track network facility construction, and providing lifting direction of the track network facilities according to the grades corresponding to the track network characteristic indexes if the track network facilities in the target area are judged to be insufficient, for example, lifting the track network facilities corresponding to the indexes smaller than three levels in the track network characteristic indexes.
The slow network characteristic evaluation module is used for determining the entropy weight of each slow network characteristic index in the target area by adopting an entropy weight method, classifying each slow network characteristic index into five grades from weak to strong by adopting a fuzzy comprehensive evaluation method, multiplying the grade of each slow network characteristic index by the corresponding entropy weight and summing to obtain the evaluation result of the slow network facility. The method specifically comprises the following steps: multiplying the level of each slow network characteristic index by the corresponding entropy weight and summing to obtain a fourth summation result (evaluation result of the slow network facilities), comparing the fourth summation value with a fourth set threshold, judging that the slow network facilities in the target area are insufficient if the fourth summation value is smaller than the fourth set threshold, thereby realizing quantitative evaluation of the slow network facilities, providing more reliable basis for slow network facility construction, and providing the lifting direction of the slow network facilities according to the grades corresponding to each slow network characteristic index if the slow network facilities in the target area are judged to be insufficient, for example, lifting the slow network facilities corresponding to the indexes smaller than three levels in each slow network characteristic index.
Taking a road network characteristic evaluation module as an example, the specific process of determining the entropy weight of each road network characteristic index in the target area by adopting the entropy weight method is as follows:
firstly, normalizing each index to obtain a normalized matrix:
N＝[x ij ] n×m ；
wherein x is ij Represents the ith sample value under the jth index.
The proportion of the ith sample value in the jth index to the jth index is as follows:
the entropy value of the j-th index is:
the weight (entropy weight) of the j-th index is:
after the weight of each road network characteristic index is obtained, an evaluation grading standard is determined by adopting a fuzzy comprehensive evaluation method, 10 traffic network facility industry experts are consulted to grade the two-level index in each evaluation index system in a grading way, each index is divided into 1-5 five grades, in order to eliminate the influence of different directions among indexes, each index is uniformly divided into five grades according to the weak-to-strong traffic supply capacity, namely ' strong supply capacity ' (five grades), ' strong supply capacity ' (four grades), ' general supply capacity ' (three grades), ' weak supply capacity), ' first grade supply capacity ' (first grade supply capacity) and ' weak supply capacity ', and then the grading of 10 experts is averaged, so that the grading standard of each index can be obtained. When comprehensively evaluating the characteristics of the traffic network facilities of the target area, multiplying the level of each index data of the target area by the corresponding weight of the index and summing to obtain the comprehensive evaluation result.
The urban traffic network facility feature analysis system further comprises a data storage module which is respectively connected with the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow-running network feature analysis module and is used for storing data acquired by the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow-running network feature analysis module, and particularly storing operation features of various traffic modes in different years, quarters, peaks in the morning and evening or in specific time periods in a target area.
The urban traffic network facility feature analysis system also comprises a time self-defining module which is used for self-defining the time period for acquiring data by the road network feature analysis module, the public transport network feature analysis module, the track network feature analysis module and the slow network feature analysis module. The custom time period comprises custom date and custom time period; the custom date includes different years, quarters, workdays, and non-workdays within the target area, and the custom time period includes an early-late peak time period or a specific time period.
The urban traffic network facility feature analysis system further comprises a granularity selection module which is respectively connected with the road network feature analysis module, the public traffic network feature analysis module, the track network feature analysis module and the slow-going network feature analysis module and is used for supporting the road network feature analysis module, the public traffic network feature analysis module, the track network feature analysis module and the slow-going network feature analysis module to acquire data from county level, street level, traffic cell and grid level.
The urban traffic network facility feature analysis system further comprises a data export module which is respectively connected with the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow network feature analysis module and is used for exporting data acquired by the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module and the slow network feature analysis module in a target area in a custom time period; the data exported by the data export module are displayed in the form of a data table, a data change curve, a picture or a map.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. An urban traffic network facility feature analysis system, comprising:
the road network characteristic analysis module is used for acquiring road network characteristic indexes in a target area, wherein the road network characteristic indexes comprise road network static characteristic indexes and road network dynamic characteristic indexes, the road network static characteristic indexes comprise road network connectivity, broken road number, road network density, intersection distance and intersection level difference, and the road network dynamic characteristic indexes comprise traffic running indexes, running average speed, running minimum speed and congestion duration;
the bus network characteristic analysis module is used for acquiring bus network characteristic indexes in a target area, wherein the bus network characteristic indexes comprise bus network static characteristic indexes and bus network dynamic characteristic indexes, the bus network static characteristic indexes comprise bus line mileage, bus network density, bus station spacing and bus station number, and the bus network dynamic characteristic indexes comprise bus average running speed and bus speed difference;
the track network characteristic analysis module is used for acquiring track network static characteristic indexes in the target area, wherein the track network static characteristic indexes comprise track mileage, track line density, track station spacing and track station number;
the slow network characteristic analysis module is used for acquiring slow network characteristic indexes in a target area, wherein the slow network characteristic indexes comprise slow network static characteristic indexes and slow network dynamic characteristic indexes, the slow network static characteristic indexes comprise bicycle special road mileage, bicycle service site quantity and bicycle parking space quantity, and the slow network dynamic characteristic indexes comprise bicycle quantity;
the data statistics analysis module is respectively connected with the road network characteristic analysis module, the public transportation network characteristic analysis module, the track network characteristic analysis module and the slow-running network characteristic analysis module; the data statistics analysis module comprises a first static index data display unit, a first dynamic index data display unit, a second static index data display unit, a second dynamic index data display unit, a third static index data display unit, a fourth static index data display unit and a third dynamic index data display unit;
the first static index data display unit is used for displaying the static characteristic index of the road network in the self-defined analysis granularity area in the self-defined time period, and the first dynamic index data display unit is used for displaying the dynamic characteristic index of the road network in the self-defined analysis granularity area in the self-defined time period; granularity includes county level, street level, traffic cell, and grid level;
the second static index data display unit is used for displaying the static characteristic index of the public transportation network in the self-defined analysis granularity area in the self-defined time period, and the second dynamic index data display unit is used for displaying the dynamic characteristic index of the public transportation network in the self-defined analysis granularity area in the self-defined time period;
the third static index data display unit is used for displaying the static characteristic index of the track network in the self-defined analysis granularity area in the self-defined time period;
the fourth static index data display unit is used for displaying the static characteristic index of the slow network in the self-defined analysis granularity area in the self-defined time period, and the third dynamic index data display unit is used for displaying the dynamic characteristic index of the slow network in the self-defined analysis granularity area in the self-defined time period.
2. The urban traffic network facility feature analysis system according to claim 1, further comprising:
the road network characteristic evaluation module is used for determining the entropy weight of each road network characteristic index in the target area by adopting an entropy weight method, grading each road network characteristic index by adopting a fuzzy comprehensive evaluation method, multiplying the corresponding entropy weight by the level of each road network characteristic index and summing the multiplied corresponding entropy weight to obtain an evaluation result of road network facilities;
the public transport network characteristic evaluation module is used for determining the entropy weight of each public transport network characteristic index in the target area by adopting an entropy weight method, classifying each public transport network characteristic index by adopting a fuzzy comprehensive evaluation method, multiplying the level of each public transport network characteristic index by the corresponding entropy weight and summing up to obtain an evaluation result of public transport network facilities;
the track network characteristic evaluation module is used for determining the entropy weight of each track network static characteristic index in the target area by adopting an entropy weight method, classifying each track network static characteristic index by adopting a fuzzy comprehensive evaluation method, multiplying the corresponding entropy weight by the level of each track network static characteristic index and summing the corresponding entropy weight to obtain an evaluation result of track network facilities;
the slow network characteristic evaluation module is used for determining the entropy weight of each slow network characteristic index in the target area by adopting an entropy weight method, classifying each slow network characteristic index by adopting a fuzzy comprehensive evaluation method, multiplying the corresponding entropy weight by the level of each slow network characteristic index and summing the corresponding entropy weight to obtain the evaluation result of the slow network facility.
3. The urban traffic network facility feature analysis system according to claim 1, further comprising a data storage module for storing data acquired by the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module, and the slow-going network feature analysis module.
4. The urban traffic network facility feature analysis system according to claim 1, further comprising a time customization module configured to customize a period of time for which the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module, and the slow network feature analysis module acquire data, the customization period of time including a customization date and a customization period of time; the custom date includes year, quarter, working day and non-working day, and the custom time period includes early peak time period, late peak time period and minor period.
5. The urban traffic network facility feature analysis system according to claim 1, further comprising a granularity selection module respectively connected to the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module, and the slow-going network feature analysis module for supporting data acquisition from a specified spatial granularity by the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module, and the slow-going network feature analysis module.
6. The urban traffic network facility feature analysis system according to claim 5, wherein the granularity selection module is connected to the data statistics analysis module for displaying data from a specified spatial granularity by a first static index data display unit, a first dynamic index data display unit, a second static index data display unit, a second dynamic index data display unit, a third static index data display unit, a fourth static index data display unit, and a third dynamic index data display unit.
7. The urban traffic network facility feature analysis system according to claim 5, further comprising a data deriving module respectively connected to the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module, and the slow network feature analysis module for deriving data obtained by the road network feature analysis module, the public transportation network feature analysis module, the track network feature analysis module, and the slow network feature analysis module for specifying spatial granularity within a custom time period.
8. The urban traffic network facility feature analysis system according to claim 7, wherein the data derived by the data deriving module is displayed in the form of a data table, a data change curve, a picture, or a map.
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