CN113421418A - Method for evaluating urban group based on utilization indexes and traffic flow data - Google Patents
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Abstract
The invention discloses a method for evaluating an urban group based on indexes and traffic flow data, which comprises the steps of obtaining vehicle networking data reported by passenger vehicles and commercial vehicles, evaluating the urban group according to the indexes of the passenger vehicles and the commercial vehicles respectively, and calculating the indexes by using passenger vehicle traffic flows and truck traffic flows respectively. The invention has the beneficial effects that: and strong data result support is provided for evaluating the economic development condition of the urban population. Through special data of inter-city traffic flows and a model calculation method, indexes for evaluating the urban groups, which are direct, intuitive and easy to understand, can be calculated, so that the urban groups are evaluated more scientifically and comprehensively.
Description
Technical Field
The invention relates to a method for evaluating urban groups based on indexes of passenger vehicles and commercial vehicles, in particular to a method based on big data and space positioning of an internet of vehicles, and belongs to the technical field of economic index research.
Background
Cities are mobile and subject to some rules; the big data can help us to observe the fineness of the city block and look down the macro of the city system. The urban group is the highest spatial organization form of the city developing to the mature stage, and refers to an urban group which generally takes more than 1 super-large city as a core, takes more than 3 large cities as a constituent unit, is formed by relying on developed infrastructure networks such as traffic communication and the like, has compact spatial organization and close economic connection, and finally realizes high city sharing and high integration.
Since the urban population emphasizes the close connection between cities, the urban population can be captured by the big data of people flow or logistics, and some indexes can be calculated by the big data of people flow and logistics to evaluate the urban population. The existing evaluation method is inaccurate, difficult to understand intuitively and poor in applicability, and firstly, the existing evaluation method only can be used for evaluation by some indirect indexes such as price integration degree due to the lack of 'flow' data capable of representing economic trade traffic among cities. Second, some big data-based assessments, often captured by pure computer professionals, traffic planning professionals, or economics, are difficult to do alone because of the breakpoints between each other; meanwhile, as the urban groups relate to the communication of people and goods among cities, the urban groups can be accurately evaluated only by considering the relation between people flow and logistics among the cities, and the big data on the market can be calculated and evaluated only based on certain data such as mobile phones and population movement, which is very lopsided. Based on the method, the method for evaluating the urban group based on the passenger vehicle flow and the commercial vehicle flow is provided.
Disclosure of Invention
The invention aims to provide a method for evaluating an urban group based on utilization indexes and traffic flow data in order to solve the problem.
The invention realizes the purpose through the following technical scheme: a method for evaluating an urban group based on utilization indexes and traffic flow data comprises the following steps:
step one, defining the connection density in an urban group according to traffic flow data, acquiring vehicle networking data reported by vehicles, acquiring travel data of which the vehicle time lasts for one year according to the reporting time of the vehicle networking data of passenger vehicles and commercial vehicles, and analyzing feature data according to the acquired reported data, wherein the starting point and the ending point of a positioning travel are judged, and event features are extracted, so that the connection density in the urban group is defined according to the traffic flow data;
step two, defining the degree of the connection between the city group and the city outside the group according to the traffic flow data, acquiring the vehicle networking data reported by the vehicle, acquiring the travel data of which the vehicle time lasts for one year according to the time of reporting the vehicle networking data of the passenger vehicle and the commercial vehicle, analyzing the characteristic data according to the acquired reported data, judging and positioning the starting point and the ending point of the travel, and extracting the event characteristics, thereby defining the degree of the connection between the city group and the city outside the group according to the traffic flow data;
step three, defining the degree of contact among the urban groups according to the calculated and summarized traffic flow data, and defining the degree of contact among the urban groups;
as a still further scheme of the invention: in the first step and the second step, the obtained car networking data comprises: in order to provide service more quickly, only necessary data need to be provided, wherein the necessary data includes license plate number, journey number, starting time, starting point longitude, starting point latitude, ending point longitude and ending point latitude.
As a still further scheme of the invention: in the first step and the second step, according to the reported data, the acquired characteristic data of the internet of vehicles is as follows: according to the time reported by the Internet of vehicles data, travel data and satellite positioning data lasting for one year of the vehicle time are obtained, the obtained satellite positioning data are analyzed, and the satellite positioning time and the satellite positioning longitude and latitude of the data points are mainly analyzed.
As a still further scheme of the invention: in the first step and the second step, the analyzed extracted event characteristics mean that one passenger car extracts characteristics capable of representing the driving behaviors of the vehicle from satellite positioning data of one year at least.
As a still further scheme of the invention: in the first step and the second step, the behavior event characteristics analyzed include: the characteristics of the travel mark, the large travel starting point, the large travel end point and the start and end city positioning city.
As a still further scheme of the invention: in the first step and the second step, when data screening processing is performed, a journey with distance of 500 meters and other unnecessary indexes are deleted according to the satellite positioning data, and the starting point and the ending point of all large journeys are defined.
As a still further scheme of the invention: in the first step, the city group interconnection density is defined according to the traffic flow data: according to the car networking data, defining 'average connection between cities', defining 'connection with a core city', defining 'connection with a regional center city', and defining 'connection between common cities'.
As a still further scheme of the invention: in the second step, the connection degree between the city group and the city outside the group is defined according to the traffic flow data: according to the data of the internet of vehicles, defining 'total traffic flow', defining 'average traffic flow', and defining 'proportion of extra-urban traffic flow to total traffic flow'.
As a still further scheme of the invention: and in the third step, data analysis and processing are carried out according to the summarized total traffic flow, and the analyzed extracted event characteristics refer to the characteristics of the total traffic flow between the cities of the two city groups.
The invention has the beneficial effects that: the method for evaluating the urban group based on the indexes and the traffic flow data is reasonable in design, combines big data with some analysis methods, summarizes social scientific rules related to urban development, and provides a beneficial guide for policy evaluation; the method comprises the steps of obtaining vehicles according to reported internet-of-vehicles data, combining various software for cooperation, city and traffic background knowledge and data mining methods, accurately identifying the location of a starting-end city and a finishing-end city, and then defining a final city group assessment method through a spatial algorithm.
Drawings
FIG. 1 is a schematic structural view of the present invention;
fig. 2 is a passenger car: an inter-group inter-city average traffic flow graph;
fig. 3 is a truck: an inter-group inter-city average traffic flow graph;
fig. 4 is a passenger car: average traffic flow diagrams of intra-group cities and core cities;
fig. 5 is a truck: average traffic flow diagrams of intra-group cities and core cities;
fig. 6 is a passenger car: average traffic flow graph between common cities in the group;
FIG. 7 is a passenger car: average traffic flow graph between common cities in the group;
fig. 8 is a passenger car: urban group external general traffic flow diagram;
fig. 9 is a truck: urban group external general traffic flow diagram;
fig. 10 is a passenger car: a proportion graph of the outside traffic flow to the total traffic flow;
fig. 11 is a truck: a proportion graph of the outside traffic flow to the total traffic flow;
fig. 12 is a passenger car: average traffic flow graph between city group and city outside the group;
fig. 13 is a truck: average traffic flow graph between city group and city outside the group;
fig. 14 is a passenger car: traffic flow diagrams between city groups;
fig. 15 is a truck: traffic flow graph between urban groups.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, a method for estimating an urban group based on utilization index and traffic data includes the following steps:
step one, defining the connection density in an urban group according to traffic flow data, acquiring vehicle networking data reported by vehicles, acquiring travel data of which the vehicle time lasts for one year according to the reporting time of the vehicle networking data of passenger vehicles and commercial vehicles, and analyzing feature data according to the acquired reported data, wherein the starting point and the ending point of a positioning travel are judged, and event features are extracted, so that the connection density in the urban group is defined according to the traffic flow data;
step two, defining the degree of the connection between the city group and the city outside the group according to the traffic flow data, acquiring the vehicle networking data reported by the vehicle, acquiring the travel data of which the vehicle time lasts for one year according to the time of reporting the vehicle networking data of the passenger vehicle and the commercial vehicle, analyzing the characteristic data according to the acquired reported data, judging and positioning the starting point and the ending point of the travel, and extracting the event characteristics, thereby defining the degree of the connection between the city group and the city outside the group according to the traffic flow data;
step three, defining the degree of contact among the urban groups according to the calculated and summarized traffic flow data, and defining the degree of contact among the urban groups;
further, in the embodiment of the present invention, in the first step and the second step, the obtained internet of vehicles data includes: in order to provide service more quickly, only necessary data need to be provided, wherein the necessary data includes license plate number, journey number, starting time, starting point longitude, starting point latitude, ending point longitude and ending point latitude.
Further, in the embodiment of the present invention, in the first step and the second step, according to the reported data, the obtained car networking feature data is: according to the time reported by the Internet of vehicles data, travel data and satellite positioning data lasting for one year of the vehicle time are obtained, the obtained satellite positioning data are analyzed, and the satellite positioning time and the satellite positioning longitude and latitude of the data points are mainly analyzed.
Further, in the embodiment of the present invention, in the first step and the second step, the extracted event characteristics analyzed are characteristics that a passenger vehicle extracts from satellite positioning data of a year at least and can represent driving behaviors of the vehicle.
Further, in the embodiment of the present invention, in the first step and the second step, the behavior event characteristics analyzed include: the characteristics of the travel mark, the large travel starting point, the large travel end point and the start and end city positioning city.
Further, in the embodiment of the present invention, in the first step and the second step, when the data filtering process is performed, the journey with distance of less than 500 m and other unnecessary indicators are deleted according to the satellite positioning data, and the start and end points of all large journeys are defined.
Further, in the embodiment of the present invention, in the first step, the density of the urban group interconnection line is defined according to the traffic data: according to the car networking data, defining 'average connection between cities', defining 'connection with a core city', defining 'connection with a regional center city', and defining 'connection between common cities'.
Further, in the embodiment of the present invention, in the second step, a degree of connection between the city group and the city outside the group is defined according to the traffic data: according to the data of the internet of vehicles, defining 'total traffic flow', defining 'average traffic flow', and defining 'proportion of extra-urban traffic flow to total traffic flow'.
Further, in the third step of the present invention, data analysis and processing are performed according to the aggregated total traffic flow, and the extracted event features that are analyzed refer to features of the total traffic flow between cities of two city groups.
Example two
Referring to fig. 2 to 15, a method for evaluating an urban group based on utilization indexes and traffic data includes the following steps:
the method comprises the following steps: defining urban cluster inline tie density based on traffic data
1. Definition of "average connections between cities"
Where F represents traffic flow, C represents a city group, i and j represent cities, and n represents the number of cities within the city group. The flow of traffic has a direction, so FijRepresenting the flow of traffic from city i to city j.
2. Definitions "Association with core City"
Wherein jhIs the core city, M is the core city number in the city group, d represents the distance between cities, and the rest is the same as above.
3. Definitions "connection to regional center City"
Wherein jqIs the region center city, N is the number of the region center cities in the city group, and the rest is the same as above.
4. Connections between common cities
The symbols have the same meanings as above.
Step two: defining the degree of relation between city group and city outside the group according to traffic data
1. Definition of "Total traffic flow"
Wherein p represents the p-th urban group, C 'represents a non-urban group, C'pAnd K represents the number of Chinese cities for analysis and the number of traffic flows, and the values of different calculation formulas K are different. The remaining symbols have the same meanings as above.
2. Definition of the proportion of the traffic flow outside the city to the total traffic flow "
The symbols have the same meanings as above.
3. Definition of "average traffic flow"
The symbols have the same meanings as above.
Step three: definition of degree of connection between City groups "
The symbols have the same meanings as above.
Step four: data verification supplement
For the verification and discovery of the data, the result is in accordance with common knowledge, and a plurality of beneficial evaluations and discoveries can be made;
first, city group internal connection density
Average connection between cities
2-3, because of the influence of distance attenuation, the average traffic flow between cities in a large city group is rather small, the traffic flow between cities in a small city group is large, and the area of the shaded part is shown, which indicates that the boundary of the large city group cannot be too wide, otherwise the economic connection compactness is reduced; however, if how many cities are in the city group, the traffic flow between cities in the traditional city group such as the long triangle and the bead triangle is still the largest.
Truck results are similar to passenger cars, but two points are noteworthy: firstly, in traditional urban groups such as Jingjin Ji, Long triangle, bead triangle and the like, the flow of trucks in urban areas is large, the economic connection is tight, and the results are similar whether the urban scale is considered or not; secondly, except for 7 relatively mature urban groups, the urban group in the peninsula in Shandong is the only urban group with higher traffic density, and is worth paying attention.
(II) connection to core City
Fig. 4 to 5 show that, due to the influence of distance attenuation, the average traffic flow with respect to the core city in a large city group is relatively low, while a small city group is relatively high. Even so, this index reflects in part the cohesion of the urban mass.
In contrast to the previous analysis, the average traffic flow of the core cities of the traditional cities, such as the bead triangle city group, the long triangle city group, the Yu-forming city group, the Central plains city group, the Guanzhong plain city group, etc., is still higher, especially the bead triangle and Guanzhong plain city group have strong external radiation force. And several newer small city groups, such as Hubao Yu, Tianshan northern slope, Dian Zhong, Qian Zhong city groups, are still in a better radiation range of the core city due to the fact that the city group has a smaller scale and higher average traffic flow ratio than the core city.
However, in the indexes of trucks, the cohesion and radiation capacity of the core cities in the traditional cities, especially in Jingjin Ji, Long triangle, Pearl triangle and the middle and former cities, are much higher than those of the newer cities.
The index of the Changjiang river midstream city group is lower, even other indexes are lower, because the economic exchange is not close among a plurality of city small groups taking Wuhan, Changsha and Nanchang as the core.
(III) connection between common cities
In fig. 6-7, from the view of the flow of the passenger car, the connection among a plurality of small city cities, such as Hubao Yu, Shandong peninsula and Qian middle city, is relatively tight. The difference of the connection degree between other cities and common cities is not great, which on one hand indicates that the boundaries of the cities are relatively reasonable, and on the other hand also indicates that the large cities such as the traditional Jingjin Ji, Long triangle and bead triangle cities exclude the peripheral cities, the common cities which are relatively close to each other have larger traffic flow and more compact economic connection.
From the truck flow, the connection between ordinary cities is similar to a passenger car, but there are several differences: 1. although the Shandong peninsula city group is a small-scale city group and generally has large traffic flow, the index result is very high, which indicates that the Shandong peninsula city group is very close in economic connection; 2. the economic connection of Jingjin Ji, Long triangular and Zhu triangular cities is still higher than other cities in terms of average truck traffic flow even though the cities already include peripheral cities with smaller traffic flows.
One notable problem is that the city group consisting of several small groups, such as the midstream of the Yangtze river, Ha Chang, Lanzhou-Xining city group, is very low due to the fact that the economic exchange between the several small groups is not close.
Second, the connection compactness between the city group and the city outside the group
Total traffic flow
In fig. 8-9, the passenger cars and trucks are similar, and the economic impact of the traditional city group, such as jingjing, changqi, the midstream of the Yangtze river, and the original city group, is still much higher than that of the new city group.
However, the flow of both passenger cars and trucks is relatively large in the Shandong peninsula city group. If the distance to the external traffic is considered, the economic impact of the Shandong peninsula urban area is reduced, indicating that the urban area mainly radiates relatively close cities.
Proportion of (II) group outside traffic flow to total traffic flow
In fig. 10 to 11, although the total traffic flow to the outside is large, the traditional mature urban groups only occupy a small part of the total traffic flow.
In the proportion of the outside traffic flow to the total traffic flow, the height of the bus traffic flow is consistent with that of the truck traffic flow, and the internal circulation ratio of a plurality of mature city groups, such as Jingjin Ji, Long triangular, bead triangular, formed Yu, Yangtze river midstream and Guanzhong plain city groups, is better, and more than 75 percent of economic connection occurs in the group. While new cities, including the Shandong peninsula city, are associated in much higher proportions, from another perspective, this also suggests that these new cities lack powerful core cities within the clusters and that the cities within the clusters are less powerful.
(III) average traffic flow
Fig. 12 to 13 show that, under the influence of the city pair with a small traffic flow, the index of the small city group is generally higher, and the large city group is lower. However, 1, no matter passenger car traffic or truck traffic, no large urban group index is found to be low, which not only indicates that the average connection closeness of the urban groups to the outside economy is similar, but also indicates that the outside average economic connection of the large urban group is stronger after excluding the remote cities; 2. from the truck flow, the Jingjin Ji city group and the Chinese city group have stronger average external connection than other city groups.
Third, the connection compactness between city groups
In fig. 14 to 15, the number of traffic flows among the urban groups is classified, and the difference between different levels is maximized by adopting the natural break classification method of ArcGIS, so that different levels are classified.
As can be seen from the figure, the flow of passenger cars and trucks between the urban communities is still greatest in kyford and coastal economy. The flow of the passenger cars is largest among urban groups in coastal economic zones, and the flow of the trucks is largest among urban groups in Jingjin Ji. The connection between the middle city group and "Jingjin Ji and coastal city group" is inferior. The connections between the western, northeast and frontier cities and other cities are weak.
If the coastal and middle cities are connected to be a bow, the connection of the bow is the most compact. Wherein, the south of the bus traffic is bigger, and the north of the truck traffic is denser.
The working principle is as follows: in using this method for estimating a city group based on the utilization index and the traffic data, first, within the city group, the average relationship between cities can be considered as the closeness of the relationship in the general sense within the city group. Meanwhile, the urban clusters often have core cities and regional center cities, and the cohesion and the boundary of the urban clusters can be seen by evaluating the connection compactness between the core cities and the regional center cities and the connection compactness between common cities.
Secondly, the urban group not only needs to examine the density and cohesion of the internal economic activity, but also needs to examine the external radiation capacity of the urban group, and is an important characteristic of the influence of the urban group. Thus, we evaluated the closeness of connection between the metropolitan clusters and the extracluster cities. Meanwhile, the proportion of the circulation in the urban group can represent the perfection degree of the urban group, so the method and the device evaluate the proportion.
Finally, the connection between the urban groups can not only allow us to see the national regional economic development bureau, but also see the important characteristics of the circulation outside the urban groups. Thus, the present application assesses the closeness of connections between urban groups.
The inspection of the results shows that the assessment of the urban groups by using the traffic flow data is intuitive, but can help people to know the urban groups more clearly, accurately and comprehensively, and has a plurality of beneficial findings. .
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (9)
1. A method for estimating an urban group based on utilization indexes and traffic data is characterized by comprising the following steps:
step one, defining the connection density in an urban group according to traffic flow data, acquiring vehicle networking data reported by vehicles, acquiring travel data of which the vehicle time lasts for one year according to the reporting time of the vehicle networking data of passenger vehicles and commercial vehicles, and analyzing feature data according to the acquired reported data, wherein the starting point and the ending point of a positioning travel are judged, and event features are extracted, so that the connection density in the urban group is defined according to the traffic flow data;
step two, defining the degree of the connection between the city group and the city outside the group according to the traffic flow data, acquiring the vehicle networking data reported by the vehicle, acquiring the travel data of which the vehicle time lasts for one year according to the time of reporting the vehicle networking data of the passenger vehicle and the commercial vehicle, analyzing the characteristic data according to the acquired reported data, judging and positioning the starting point and the ending point of the travel, and extracting the event characteristics, thereby defining the degree of the connection between the city group and the city outside the group according to the traffic flow data;
and step three, defining the degree of contact among the urban groups according to the calculated and summarized traffic flow data, and defining the degree of contact among the urban groups.
2. The method of claim 1, wherein the method comprises the following steps: in the first step and the second step, the obtained car networking data comprises: in order to provide service more quickly, only necessary data need to be provided, wherein the necessary data includes license plate number, journey number, starting time, starting point longitude, starting point latitude, ending point longitude and ending point latitude.
3. The method of claim 1, wherein the method comprises the following steps: in the first step and the second step, according to the reported data, the acquired characteristic data of the internet of vehicles is as follows: according to the time reported by the Internet of vehicles data, travel data and satellite positioning data lasting for one year of the vehicle time are obtained, the obtained satellite positioning data are analyzed, and the satellite positioning time and the satellite positioning longitude and latitude of the data points are mainly analyzed.
4. The method of claim 1, wherein the method comprises the following steps: in the first step and the second step, the analyzed extracted event characteristics mean that one passenger car extracts characteristics capable of representing the driving behaviors of the vehicle from satellite positioning data of one year at least.
5. The method of claim 1, wherein the method comprises the steps of: in the first step and the second step, the behavior event characteristics analyzed include: the characteristics of the travel mark, the large travel starting point, the large travel end point and the start and end city positioning city.
6. The method of claim 1, wherein the method comprises the steps of: in the first step and the second step, when data screening processing is performed, a journey with distance of 500 meters and other unnecessary indexes are deleted according to the satellite positioning data, and the starting point and the ending point of all large journeys are defined.
7. The method of claim 1, wherein the method comprises the steps of: in the first step, the city group interconnection density is defined according to the traffic flow data: according to the car networking data, defining 'average connection between cities', defining 'connection with a core city', defining 'connection with a regional center city', and defining 'connection between common cities'.
8. The method of claim 1, wherein the method comprises the steps of: in the second step, the connection degree between the city group and the city outside the group is defined according to the traffic flow data: according to the data of the internet of vehicles, defining 'total traffic flow', defining 'average traffic flow', and defining 'proportion of extra-urban traffic flow to total traffic flow'.
9. The method of claim 1, wherein the method comprises the steps of: and in the third step, data analysis and processing are carried out according to the summarized total traffic flow, and the analyzed extracted event characteristics refer to the characteristics of the total traffic flow between the cities of the two city groups.
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