CN109345434B - Method for evaluating design safety of external roads in open type community - Google Patents
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Abstract
The invention discloses a method for evaluating the design safety of external roads in an open community, which comprises the following steps: (1) dividing survey areas and acquiring data; (2) screening research objects; (3) dividing and distinguishing road network forms; (4) constructing a traffic safety analysis model; (5) and (5) evaluating traffic safety. The invention has the beneficial effects that: the method comprises the steps of screening a region with similar road network characteristics and land use characteristics with an open cell as a research object, researching the influence of road design on cells with different road network forms by using a full Bayesian spatial hierarchical model, introducing a random effect term considering cell spatial correlation on the basis of a random error term, and considering the convergence effect of the cells on the space. In addition, the model carries out quantitative analysis and grade division on the traffic safety level after the community is opened, and a scientific and effective method is provided for the traffic safety risk identification and the traffic safety level evaluation of the community opening.
Description
Technical Field
The invention relates to the technical field of road traffic, in particular to a method for evaluating the design safety of external roads in an open community.
Background
With the rapid development of national economy and the acceleration of urbanization process in China, the problem of traffic jam in some cities is increasingly serious. The unreasonable structure of urban traffic network in China is the main reason that the problem cannot be effectively solved. For a long time, due to the limitation of planning ideas and management systems, cities in China have a plurality of large closed cells, and the cells obstruct the formation of an urban microcirculation road network to a certain extent, cut urban traffic and increase the pressure of the urban road network. The key is to solve the traffic jam of the urban main road, promote the residential block system and gradually open the established residential districts and unit colleges. The biggest problem in the process of pushing the community to be opened is how to ensure the road traffic safety of the opened community.
Different from the established research basis of the open street road network security planning in foreign countries, the concept of the open cell is still in the popularization stage in China. In the research field of scientific research or patent application field, the research based on the open cell often utilizes the topological theory, traffic flow theory and the like to analyze the aspects of road network reliability, accessibility, traffic jam and the like, and the research on the safety evaluation of the road network, especially the safety evaluation related to the design of the internal and external roads of the open cell, is still quite lacking. Aiming at the contradiction between the practical social demand and the insufficient theoretical research, the invention provides a method for designing and evaluating the safety of external roads in an open cell.
Disclosure of Invention
In order to solve the existing problems, the invention provides a method for designing and evaluating safety of external and internal roads in an open cell, which can quantitatively analyze the safety level designed by the external and internal roads in the open cell and grade the traffic safety level of the external and internal roads, and in order to achieve the purpose, the invention provides a method for designing and evaluating safety of the external and internal roads in the open cell, which comprises the following steps:
(1) dividing survey areas and collecting data: dividing the region by using the main trunk road and the secondary trunk road as boundaries, and selecting the region with the area larger than 0.5km2The area of the system is used as an investigation area, a main road, a secondary road and a branch road are used as the sides of a road network in the investigation area, a road intersection point and a broken road end point are used as nodes for data acquisition, and the data acquisition comprises a residential land occupation ratio prDensity d of road networkrMajor road ratio PAMinor road ratio PSNumber of edges E, number of nodes N of the network, degree k of node NnDistance d of nodes i and jijNumber k of shortest paths between nodesijRoad network connectivity c, road speed limit vLLength of road section l, number of external paths of cell CENumber of accidents a, traffic q;
(2) study ofScreening objects: introducing a screening index, selecting a road network with the residential land occupation ratio of more than 25% and the road network density of 8-12 km/km2And the investigation region with the connectivity larger than 1.6 is taken as a research object, namely a traffic safety analysis cell, and the road network characteristic variables of each traffic safety analysis cell and the adjacent investigation regions are calculated by using the data obtained in the step (1), wherein the road network characteristic variables comprise the shortest path number k between the nodes i and j and passing through the node ninjIntermediate centrality BnNear centrality CnGrid coefficient M;
(3) road network form division and discrimination: will be the main road ratio PAMinor road ratio PSIntermediate centrality BnNear centrality CnThe grid coefficient M is used as a clustering variable, and the road network forms of all traffic safety analysis cells and adjacent investigation regions thereof are divided into g classes represented as R by a K-means clustering methodf(f ═ 1,2,3, …, g). Judging the road network form of the region by a Bayesian discriminant analysis method according to the clustering result, wherein the network form with the highest discrimination score is the road network form of the region, SfA discrimination score, s, representing the f-th type road network formoDenotes a constant term, skRepresenting the cluster variable coefficient, XkRepresenting a clustering variable, the corresponding discriminant function is:
(4) evaluation model selection and parameter calibration: taking the accident rate of a traffic analysis community as a dependent variable, and adopting full Bayesian spatial stratificationThe model performs a traffic safety analysis, I denotes the total number of investigation regions adjacent to the traffic analysis cell, Nf(f ═ 1,2,3, …, g) denotes R adjacent to the traffic analysis cellfNumber of survey-like areas, CERepresenting the number of outward paths, K representing the set of links of the investigation region, VLRepresenting the total average speed limit difference between the traffic analysis cell and the adjacent investigation region, α is a constant term,iRepresenting a random error term, uiRandom effect terms representing spatial correlation, βnRespectively establishing traffic safety analysis models for the traffic analysis cells with different road network forms for regression vector coefficients;
(5) and (3) traffic safety evaluation: dividing the district into road network forms, carrying out quantitative analysis on the traffic safety level after the district is opened by utilizing the model of the corresponding road network form, obtaining the total distribution condition of the expected accident rate of all the districts, and respectively translating upwards and downwards by taking the expected accident rate mu as a central line according to the characteristics of the model
The unit is used as an upper control limit and a lower control limit, and the traffic safety level designed by the external roads in the community is divided into four levels: i, II, III and IV.
In a further development of the invention, the degree k of the node n in step (1)nThe link degree is the ratio of the number of road links to the number of road nodes in the investigation region.
The invention relates to a safety evaluation method for external road design in an open cell, which is characterized in that an area with the similar land property as the existing closed cell is selected as a research object, a full Bayesian spatial hierarchical model is utilized to research the influence of road design on cells with different road network forms, a random effect item considering the spatial correlation of the cells is introduced on the basis of a random error item, and the spatial convergence effect of the open cell is considered. In addition, the model carries out quantitative analysis and grade division on the traffic safety level of the opened closed cell, and a scientific and effective method is provided for the traffic safety risk identification and the traffic safety level evaluation of the open cell.
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Fig. 1 is a schematic diagram of traffic safety analysis results and safety evaluation grade classification according to the present invention.
FIG. 2 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a method for evaluating the design safety of external roads in an open cell, which can carry out quantitative analysis on the safety level of the design of the external roads in the open cell and carry out grade division on the traffic safety level.
As shown in fig. 1 and 2, a method for determining the influence of an expressway fixed-point speed meter on the number of traffic accidents includes the following steps:
(1) dividing survey areas and collecting data: road network information and land utilization information are researched and collected by a local traffic department and a traffic police station, a main road and a secondary road are used as boundaries to divide areas, and the area is selected to be larger than 0.5km2The area of (2) is used as an investigation area. Taking a main road, a secondary main road and a branch road as the sides of a road network in an investigation region, taking a road intersection point and a broken road end point as nodes for data acquisition, wherein the data acquisition comprises a residential land occupation ratio prDensity d of road networkrMajor road ratio PAMinor road ratio PSNumber of edges E, number of nodes N of the network, degree k of node NnDistance d of nodes i and jijNumber k of shortest paths between nodesijRoad network connectivity c, road speed limit vLLength of road section l, number of external paths of cell CENumber of accidents a, traffic volume q.
(2) Screening of study subjects: introducing a screening index, selecting a road network with the residential land occupation ratio of more than 25% and the road network density of 8-12 km/km2And the investigation area with the connectivity degree larger than 1.6 is taken as a research object, namely a traffic safety analysis cell. And calculating road network characteristic variables of each traffic safety analysis cell and adjacent investigation regions thereof by using the data obtained in the step (1), wherein the road network characteristic variables comprise the shortest path number k between the nodes i and j and passing through the node ninjIntermediate centrality BnNear centrality CnGrid coefficient M.
(3) Road network form division and discrimination: will be the main road ratio PAMinor road ratio PSIntermediate centrality BnNear centrality CnThe grid coefficient M is used as a clustering variable, and the road network forms of all traffic safety analysis cells and adjacent investigation regions thereof are divided into g classes represented as R by a K-means clustering methodf(f ═ 1,2,3, …, g). Judging the road network form of the region by a Bayesian discriminant analysis method according to the clustering result, wherein the network form with the highest discrimination score is the road network form of the region, SfA discrimination score, s, representing the f-th type road network formoDenotes a constant term, skRepresenting the cluster variable coefficient, XkRepresenting a clustering variable, the corresponding discriminant function is:
(4) evaluation model selection and parameter calibration: taking the accident rate of a traffic analysis cell as a dependent variable, and adoptingAnd carrying out traffic safety analysis by using the full Bayesian spatial hierarchical model. I denotes the total number of investigation regions adjacent to the traffic analysis cell, Nf(f ═ 1,2,3, …, g) denotes R adjacent to the traffic analysis cellfNumber of survey-like areas, CERepresenting the number of outward paths, K representing the set of links of the investigation region, VLRepresenting the total average speed limit difference between the traffic analysis cell and the adjacent investigation region, α is a constant term,iRepresenting a random error term, uiRandom effect terms representing spatial correlation, βnAre regression vector coefficients. And respectively establishing traffic safety analysis models for traffic analysis cells with different road network forms.
(5) And (3) traffic safety evaluation: and carrying out road network form division on the cells, carrying out quantitative analysis on the traffic safety level after the cells are opened by utilizing the model of the corresponding road network form, and obtaining the total distribution condition of the expected accident rate of all the cells. According to the characteristics of the model, the expected accident rate mu is taken as a central line, and the model respectively translates upwards and downwards
The unit is used as an upper control limit and a lower control limit, and the traffic safety level designed by the external roads in the community is divided into four levels: i, II, III and IV.
The present invention will be described with reference to specific examples.
1) Dividing survey areas and collecting data: road network information and land utilization information are researched and collected by a local traffic department and a traffic police station, a main road and a secondary road are used as boundaries to divide areas, and the area is selected to be larger than 0.5km2The area (2) is used as a survey area, and each survey area sample number is zi. Using main road, secondary road and branch road as side of road network in investigation region and road cross pointData collection is performed with the end point of the broken end as a node, and the obtained relevant data of each investigation region is shown in table 1-1.
TABLE 1-1 survey area data acquisition statistics
2) Screening of study subjects: introducing a screening index, selecting a road network with the residential land occupation ratio of more than 25% and the road network density of 8-12 km/km2And the investigation region with the ratio of the road connection quantity to the road node quantity larger than 1.6 is taken as a research object, namely a traffic safety analysis cell.
3) Road network form division: will be the main road ratio PAMinor road ratio PSIntermediate centrality BnNear centrality CnThe grid coefficient M is used as a clustering variable, and the road network forms of all traffic safety analysis cells and adjacent investigation regions thereof are divided into g classes represented as R by a K-means clustering methodf(f ═ 1,2,3, …, g). And judging the road network form of the region by a Bayesian judgment analysis method according to the clustering result, wherein the network form with the highest judgment score is the road network form of the region.
4) Constructing a traffic safety analysis model: based on the division result of the road network forms, counting the number of investigation regions of various road network forms adjacent to the traffic safety analysis cell, obtaining an independent variable data table 1-2 of a traffic safety analysis cell traffic safety analysis model according to data in the table 1-1, taking the accident rate of the traffic analysis cell as a dependent variable, and respectively establishing the traffic safety analysis model for the traffic analysis cells with different road network forms by adopting a Bayesian spatial hierarchical model by utilizing the data in the table 1-2.
TABLE 1-2 traffic safety analysis neighborhood quantity acquisition statistical table
5) And (3) traffic safety evaluation: and carrying out road network form division on the cells, and carrying out quantitative analysis on the opened traffic safety level by using a model corresponding to the road network form to obtain the total distribution condition of the expected accident rate of the whole cell. Taking expected accident rate as central line, respectively translating upwards and downwards
The unit is used as an upper control limit and a lower control limit, and the traffic safety level designed by the external roads in the community is divided into four levels: i, II, III and IV.
Table 1-3 traffic safety level classification table
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (2)
1. A method for designing and evaluating safety of external roads in an open cell is characterized by comprising the following steps:
(1) dividing survey areas and collecting data: dividing the region by using the main trunk road and the secondary trunk road as boundaries, and selecting the region with the area larger than 0.5km2The area of the system is used as an investigation area, a main road, a secondary road and a branch road are used as the sides of a road network in the investigation area, a road intersection point and a broken road end point are used as nodes for data acquisition, and the data acquisition comprises a residential land occupation ratio prDensity d of road networkrMajor road ratio PAMinor road ratio PSNumber of edges E, number of nodes N of the network, degree k of node NnDistance d of nodes i and jijNumber k of shortest paths between nodesijRoad network connectivity c, road speed limit vLLength of road section l, number of external paths of cellCENumber of accidents a, traffic q;
(2) screening of study subjects: introducing a screening index, selecting a road network with the residential land occupation ratio of more than 25% and the road network density of 8-12 km/km2And the investigation region with the connectivity larger than 1.6 is taken as a research object, namely a traffic safety analysis cell, and the road network characteristic variables of each traffic safety analysis cell and the adjacent investigation regions are calculated by using the data obtained in the step (1), wherein the road network characteristic variables comprise the shortest path number k between the nodes i and j and passing through the node ninjIntermediate centrality BnNear centrality CnGrid coefficient M;
(3) road network form division and discrimination: will be the main road ratio PAMinor road ratio PSIntermediate centrality BnNear centrality CnThe grid coefficient M is used as a clustering variable, and the road network forms of all traffic safety analysis cells and adjacent investigation regions thereof are divided into g classes represented as R by a K-means clustering methodf(f is 1,2,3, …, g), according to the clustering result, the road network form is judged by a Bayesian judgment analysis method, the network form with the highest judgment score is the road network form of the area, SfA discrimination score, s, representing the f-th type road network formoDenotes a constant term, skRepresenting the cluster variable coefficient, XkRepresenting a clustering variable, the corresponding discriminant function is:
(4) evaluation model selection and parameter calibration: taking the accident rate of a traffic analysis cell as a dependent variable, adopting a full Bayesian spatial hierarchical model to perform traffic safety analysis, wherein I represents the total number of investigation regions adjacent to the traffic analysis cell, Nf(f ═ 1,2,3, …, g) denotes R adjacent to the traffic analysis cellfNumber of survey-like areas, CERepresenting the number of outward paths, K representing the set of links of the investigation region, VLRepresenting the total average speed limit difference between the traffic analysis cell and the adjacent investigation region, α is a constant term,iRepresenting a random error term, uiRandom effect terms representing spatial correlation, βnRespectively establishing traffic safety analysis models for the traffic analysis cells with different road network forms for regression vector coefficients;
(5) and (3) traffic safety evaluation: dividing the district into road network forms, carrying out quantitative analysis on the traffic safety level after the district is opened by utilizing the model of the corresponding road network form, obtaining the total distribution condition of the expected accident rate of all the districts, and respectively translating upwards and downwards by taking the expected accident rate mu as a central line according to the characteristics of the model
The unit is used as an upper control limit and a lower control limit, and the traffic safety level designed by the external roads in the community is divided into four levels: i, II, III and IV.
2. The method for designing safety evaluation of external roads in open cell as claimed in claim 1, wherein: degree k of node n in step (1)nThe number of edges adjacent to the node n in the investigation region, and the connectivity of the road nodes and the number of road links in the investigation regionA ratio.
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| CN110414067B (en) * | 2019-07-01 | 2023-07-07 | 东南大学 | Method for designing intra-district and external road connection considering traffic safety |
| CN112751911B (en) * | 2020-12-15 | 2022-10-21 | 北京百度网讯科技有限公司 | Road network data processing method, device, equipment and storage medium |
| CN113449402B (en) * | 2021-06-22 | 2022-08-05 | 武汉大学 | Road network efficiency gain prediction method after broken road is opened |
| CN113920738B (en) * | 2021-10-26 | 2023-04-21 | 北京工业大学 | Urban trunk security analysis model migration method based on different country merging data |
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