CN104317583A

CN104317583A - Road congestion optimization algorithm based on grid theory

Info

Publication number: CN104317583A
Application number: CN201410561347.3A
Authority: CN
Inventors: 胡平
Original assignee: HEFEI XINGFU INFORMATION TECHNOLOGY Co Ltd
Current assignee: HEFEI XINGFU INFORMATION TECHNOLOGY Co Ltd
Priority date: 2014-10-21
Filing date: 2014-10-21
Publication date: 2015-01-28

Abstract

The invention provides a road congestion optimization algorithm based on the grid theory and relates to an optimization algorithm for congestion algorithm by positioning floating vehicle data in city road grids. The method is characterized in that city regions are divided into grids, city roads are partitioned into thin segments through the grids and are located in the corresponded grids, and the correspondences between the grids and roads are established; the floating vehicle data are classified according to the grids where the floating vehicle data belong to, the number of vehicles of each grid corresponds to the corresponded segments, and the road congestion algorithm is acquired.

Description

Based on the congestion in road optimized algorithm of grid paradigm

Technical field

The present invention proposes a kind of optimized algorithm adopting grid paradigm to carry out congestion in road analysis, is specially adapted to the jamming analysis at urban road crossing.

Background technology

At present, there is various roads in the prior art to block up algorithm.

Such as, traffic index algorithm: be carry out deep processing process to the dynamic vehicle positional information being distributed in streets and lanes, city (abbreviation floating car data) to obtain, by the vehicle GPS passback dynamic data on whole city's taxi to data processing centre (DPC).First to vehicle position data process, obtain the travelling speed of difference in functionality grade road, then and data on flows different according to function path calculates this road shared weight in the whole network, judges, be converted to the index desired value of 0-10 finally by people to the perception of blocking up.

For another example, the Theories and methods of pattern-recognition: the people such as Ren Jiangtao and Zhang Yi study the pattern in urban traffic network and highway network, drawn traffic behavior can turn to repeat, limited amount and the result of dissimilar pattern; The people such as Guo Wei and Yao Danya are extracted the proper vector of access connection traffic flow operation conditions and are set up the assessment models of traffic by intersection data similarity.These research work all achieve good effect, and the research for traffic status identification technology provides good reference.

The common feature of these patented methods extracts road vehicle data, carries out data analysis, be converted into traffic index or be fitted to traffic state model, have higher complexity.

Summary of the invention

One object of the present invention is to provide a kind of being positioned by floating vehicle data to carry out the optimization method calculated that blocks up in urban road grid, the method is first by urban area gridding, urban road is separated into tiny section by grid, lay respectively in corresponding grid, and set up grid and section one-to-one relationship.Then classified by the grid of floating vehicle data belonging to it, the vehicle number finally in each grid just corresponds on corresponding section, thus draws the computing method that section blocks up.

Described method comprises the steps:

1, first calculate the maximum longitude and latitude span in city, according to the segregation method of specifying, namely the segmentation unit of precision and latitude is per mille degree, and urban area is divided into minimum grid cell, and grid cell such as is at the uniform grid of the array arrangement of size.

2, secondly on electronic chart, these grid cells of urban road are split, forms tiny road section, just have one or more little section in each grid, and record the one-to-one relationship of these sections and grid cell.

3, subsequently, the vehicle data that floats is extracted, because each Floating Car anchor point is always in some grid cells by GPS administrative center.By the longitude and latitude of these floating vehicle datas according to its place, be referred to respectively in corresponding grid cell.The initial value of value of blocking up is 0, and grid cell often increases a vehicle data, its value of blocking up increase by 1, and the color value in the section corresponding to this grid cell is initially white and R=255, B=255, G=255.When value of blocking up often increases by 1, red component R=255 is constant, and blue component B and green component G deducts 1 respectively, until be 0.

4, last, by all grid cells exceeding threshold value of blocking up by its value of blocking up descending sort, electronic chart directly shows its corresponding road section by its color value.

The present invention is by introducing the grid cell of electronic chart, set up the corresponding relation in grid cell and section, counted by the floating vehicle dropped in grid cell, map directly to corresponding section, thus avoiding a large amount of map vector calculuses, data read in end, calculate and just terminate thereupon, improve the efficiency of blocking up and calculating greatly, for real-time road shows the computing method providing an efficient quick.

Accompanying drawing explanation

Fig. 1 is that map basic data prepares process flow diagram;

Fig. 2 is real-time jamming analysis process flow diagram;

Fig. 3 is map grid segmentation figure;

Fig. 4 is section mesh segmentation figure;

Fig. 5 is the corresponding relation figure of crossing, section and grid.

Embodiment

The gridding of the present invention algorithm that blocks up is mainly used in improving the counting yield of congestion in road, particularly high to the requirement of real-time that blocks up large-and-medium size cities, and the method can calculate the section and crossing that block up faster, facilitates people's trip.

In order to realize this grid process optimization method, first need urban area gridding, the threshold value of gridding and spacing value will be fixed, then according to grid lane segmentation got well and set up the corresponding relation at grid and crossing, section, these are all initial preliminary work, only need to prepare once, be saved in database, as shown in Figure 1.The floating vehicle position data that last basis is real-time, in conjunction with gridding threshold value and spacing value, draws the grid cell belonging to this position, thus belongs to corresponding section or crossing.Data read in end, block up to calculate and also terminate thereupon, as shown in Figure 2.

Idiographic flow is as follows:

1. in a step 101, obtain the maximum span of the longitude and latitude of urban area.

The P lower left corner=(Xmin, Ymin); Xmin: minimum longitude; Ymin: minimum latitude.

The P upper right corner=(Xmax, Ymax); Xmax: maximum longitude; Ymax: maximum latitude.

2. in a step 102, choose fixing segmentation starting point and spacing, be accurate to per mille degree, see Fig. 3.

P splits starting point=(Px splits starting point, and Py splits starting point)=(Floor (Xmin * 1000)/1000, Floor (Ymin * 1000)/1000);

P splits terminal=(Px splits terminal, and Py splits terminal)=(Ceil (Xmax * 1000)/1000, Ceil (Ymax * 1000)/1000);

Segmentation space D=0.001 degree.

3. in step 103, according to the cutting unit that step 102 obtains, urban road is cut, form little section or crossing, as shown in Figure 4.

4. at step 104, set up corresponding relation according to the result of step 103.

After over-segmentation:

Unit 1 and section A, section B correspondence

Unit 2 and crossing C correspondence

Unit 3 and section D correspondence

Shown in opening relationships chart 5.

5. in step 201, read the vehicle data that floats, its longitude and latitude data are transformed into corresponding cutting unit.

Vehicle point coordinate P(Px, Py);

First ask longitudinal n value, be designated as nj:

Nj=Floor [(Floor (Px * 1000)/1000-Px splits starting point)/D]

Ask dimension direction n value again, be designated as nw

Nw=Floor [(Floor (Py * 1000)/1000-Py splits starting point)/D]

Draw coordinate P(Px, the Py of vehicle point) belonging to cutting unit be:

AREA{Px splits starting point+nj*D, and Py splits starting point+nw*D, and Px is split starting point+(nj+1) * D, Py and split starting point+(nw+1) * D}.

6. in step 202., according to two Key values in region, find out corresponding section or crossing Polyline, the blueness of its color value and green value are deducted 1.

Suppose that original color value is RGB(Rn, Gn, Bn)

Rn=Rn；0<=Rn<=255

Gn=Gn-1；0<=Gn<=255

Bn=Bn-1；0<=Bn<=255

Repeat step 201 and 202 until all vehicle point data read end.

7. in step 203, from database read color value exceed threshold value Ф section or crossing and by it blue and green component ascending order arrange (blue component and green component less, color is redder, the congestion level represented is higher, otherwise color is more shallow, represent more unobstructed), electronic chart directly shows.

Claims

1. floating vehicle data is positioned to carry out the optimization method calculated that blocks up in urban road grid by one kind;

Floating vehicle data just refers to has installed vehicle-mounted GPS positioning system and the bus travelled on urban road and taxi;

The speciality of the method is: first by urban area gridding, and urban road is separated into tiny section by grid, lays respectively in corresponding grid, and sets up grid and section one-to-one relationship.

2. then classified by the grid of floating vehicle data belonging to it, the vehicle number finally in each grid just corresponds on corresponding section, thus draws the computing method that section blocks up.

3. described in, method comprises the steps:

1) the maximum longitude and latitude span in city, is first calculated, according to the segregation method of specifying, namely the segmentation unit of precision and latitude is per mille degree, and urban area is divided into minimum grid cell, and grid cell such as is at the uniform grid of the array arrangement of size;

2), secondly on electronic chart, these grid cells of urban road are split, forms tiny road section, just have one or more little section in each grid, and record the one-to-one relationship of these sections and grid cell;

3), subsequently, the vehicle data that floats is extracted, because each Floating Car anchor point is always in some grid cells by GPS administrative center;

4), by the longitude and latitude of these floating vehicle datas according to its place, be referred in corresponding grid cell respectively;

The initial value of value of blocking up is 0, and grid cell often increases a vehicle data, its value of blocking up increase by 1, corresponding to this grid cell 5), the color value in section is initially white and R=255, B=255, G=255;

When value of blocking up often increases by 1, red component R=255 is constant, and blue component B and green component G deducts 1 respectively, until be 0.