WO2013167085A2 - 一种基于栅格映射生长的交通网络划分方法及配置服务器 - Google Patents

一种基于栅格映射生长的交通网络划分方法及配置服务器 Download PDF

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WO2013167085A2
WO2013167085A2 PCT/CN2013/080241 CN2013080241W WO2013167085A2 WO 2013167085 A2 WO2013167085 A2 WO 2013167085A2 CN 2013080241 W CN2013080241 W CN 2013080241W WO 2013167085 A2 WO2013167085 A2 WO 2013167085A2
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grid
grids
growth
divided
division
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PCT/CN2013/080241
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French (fr)
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WO2013167085A3 (zh
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陆平
董振江
秦旭彦
史其信
付强
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中兴通讯股份有限公司
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Publication of WO2013167085A2 publication Critical patent/WO2013167085A2/zh
Publication of WO2013167085A3 publication Critical patent/WO2013167085A3/zh

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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
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • 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
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/012—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from other sources than vehicle or roadside beacons, e.g. mobile networks
    • 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
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data

Definitions

  • the invention belongs to the field of intelligent transportation, and particularly relates to a traffic network division method and a load sharing configuration server based on grid mapping growth. Background technique
  • the existing traffic network partitioning schemes have the following problems: 1) No matter which scheme can be obtained, the final partitioning result can be obtained once, and iterative adjustment and multi-layer merger can be obtained. Therefore, execution High complexity. 2) In order to reduce the complexity, the operation should not be too complicated. At present, it is usually applied to the common matrix case, because it is selected, so it is applicable in the division of traffic network. The scope is not large. 3) In the existing traffic network partitioning scheme, because the accuracy of the partitioning is not high, the load of each processor performing the partitioning operation is not balanced enough, and the parallel processing is not efficient, and the problem is even more complicated when the execution complexity is high. It is obvious. There is an urgent need for a solution with low implementation complexity, high precision, load balancing, and high parallel processing. Summary of the invention
  • the main purpose of the embodiments of the present invention is to provide a traffic network partitioning method and a load sharing configuration server based on grid mapping growth, which have low execution complexity, high precision, load balancing, and parallel processing.
  • a traffic network partitioning method based on grid mapping growth comprising:
  • the grid growth division is performed, and the corresponding grid combination is obtained.
  • the method further includes: after obtaining the corresponding grid combination, performing grid mapping restoration in the designated traffic network area, and restoring the grid combination to a corresponding traffic network block.
  • the method further includes: an initialization process, before performing raster mapping on the acquired traffic flow data of the specified traffic network area;
  • the initialization process specifically includes: performing task division according to the traffic flow data of the designated traffic network area and configuring the divided tasks to different computing servers, and determining, according to the number of the computing servers, the to-be-divided in the designated traffic network area The number of divided blocks processed.
  • the traffic flow data includes at least: length information of the side of the traffic network and topology information, and a spatial location of the intersection of the traffic network.
  • the obtaining the weight value of each grid specifically includes: when traversing all the edges of the traffic network, accumulating the length information of the edge into the corresponding grid to obtain the calculation amount of the grid and using the weight of the grid as a weight of the grid value;
  • Obtaining the degree of association between the adjacent grids specifically includes: when traversing all the edges of the traffic network, obtaining edges crossing the grid according to the topology information, and contiguous grids corresponding to the edges of the spanning grid The degree of association is accumulated to obtain the traffic of the grid and as the degree of association between the adjacent grids.
  • the weight value of each grid and the degree of association between adjacent grids after the end of the grid mapping are specifically as follows: obtaining the calculation amount of each grid, and the adjacent grids corresponding to four adjacent directions of each grid The degree of association between cells and as the traffic of the grid;
  • the performing the grid growth division specifically includes: obtaining the first non-empty grid as a starting position of the grid growth division according to the progressive scan manner, and analyzing the growth division conditions of the adjacent adjacent grids, respectively,
  • the grid of the growth division condition is marked as a divided grid, and the calculation amount and the traffic of the grid of the divided grid are added to the divided block and used as the calculation amount and the traffic of the divided block, and the setting is performed. Divide the lateral length and longitudinal length of the block until the end of the grid growth division.
  • the non-empty grid is specifically a grid in which the calculation amount of the grid and the traffic are not zero at the same time; the labeling the grid that meets the growth division condition as the divided grid one by one includes: Calculating the calculated amount and traffic of the un-divided grid and adding it to the partitioning block as the candidate location in the partitioning block, which is adjacent to each currently scanned grid. Calculating the amount of calculation and traffic of the block, if the calculated amount of the divided block is not greater than the upper limit X of the calculated amount of the divided block, the undivided grid is marked as a divided grid, and the raster is continued Scanning and growth division.
  • a load sharing configuration server based on grid mapping growth includes: a grid mapping unit and a grid growth dividing unit; wherein
  • the grid mapping unit is configured to perform grid mapping on the acquired traffic flow data of the specified traffic network area, to obtain a weight value of each grid and an association degree between adjacent grids;
  • the grid growth dividing unit is configured to perform grid growth division according to the weight value of each grid and the degree of association between adjacent grids, to obtain a corresponding grid combination.
  • the server further includes: a grid map restoring unit configured to perform grid map restoration in the specified traffic network area, and restore the grid combination to a corresponding traffic network block.
  • the traffic flow data includes at least: length information and topology information of the side of the traffic network, The spatial location of the intersection of the traffic network.
  • the grid mapping unit is further configured to: when traversing all the edges of the traffic network, accumulating the length information of the edge into the corresponding grid to obtain the calculation amount of the grid and as the weight value of the grid. Obtaining an edge across the grid according to the topology information, accumulating the degree of association of the adjacent grids corresponding to the edges of the grid to obtain the traffic of the grid and as an association between the adjacent grids degree.
  • the grid mapping unit is further configured to obtain a quantity of each grid after the grid mapping ends;
  • the grid growth dividing unit is further configured to acquire the first non-empty grid as a starting position of the grid growth division according to the progressive scanning manner, and analyze the growth division conditions of the adjacent adjacent grids, and then A grid that conforms to the growth division condition is marked as a divided grid, and the calculation amount and the traffic of the grid of the divided grid are added to the divided block and used as the calculation amount and the traffic of the divided block, Set the lateral length and longitudinal length of the divided blocks until the end of the grid growth division.
  • the embodiment of the present invention performs grid mapping on the obtained traffic flow data of the specified traffic network area, and obtains the weight value of each grid and the degree of association between adjacent grids; according to the weight values of the grids and adjacent grids The degree of correlation between the grids is divided into grids to obtain corresponding grid combinations.
  • the spatial distribution of the regular traffic network can be obtained by grid mapping, after the grid growth is divided according to the comprehensive consideration of the weight value of each grid and the correlation degree between adjacent grids, the obtained Each partition on the spatial distribution must be load balanced.
  • the execution complexity is low, the precision is high, the load is balanced, and the parallel processing is high.
  • FIG. 1 is a schematic flowchart of an implementation of a method principle according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of a grid mapping process according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of grid mapping of a road traffic network in a main city according to an embodiment of the present invention
  • FIG. 4 is a schematic diagram showing an implementation flow of a grid growth division according to an embodiment of the present invention
  • Figure 6a is a schematic diagram of W 'final partition block 1
  • Figure 6c is a schematic diagram of the final partitioning block 3 of the present invention. detailed description
  • the invention has wide application range, and is applicable not only to regular and ordinary matrix planning situations, but also to irregular traffic networks with sparse matrix characteristics.
  • the characteristics of sparse matrices often have places where rules can be found. For example, nodes are often connected to nodes that are adjacent in space, and although a city's traffic network belongs to an irregular network, most of the cases are not completely absent.
  • the graph of rules, from the process of urban development and evolution, urban transport networks tend to be relatively regular.
  • the traffic map division scheme based on the grid mapping growth in the embodiment of the present invention is generated based on such an idea, and the rules are found by analyzing the characteristics of the traffic network, and are applied to the division scheme of the embodiment of the present invention.
  • the embodiment of the present invention is a scheme proposed according to the characteristics of the traffic network and the idea of the graphic growth method.
  • the division principle Given a traffic network, it can be arbitrarily divided into n blocks, and the division of the blocks is flexible; The total amount of communication between the two should be controlled as little as possible, and the calculation amount of each block should be as equal as possible, so that the load balancing of multiple processors in calculating data can be ensured, and the parallel processing efficiency can be improved. Moreover, the scheme can be finalized once, and the final division result can be obtained. It does not require repeated iterative adjustment phases or multi-layer merging. Therefore, it is not necessary to repeatedly adjust, one-step, and simplify calculation, so that the precision is high and the execution efficiency is also high.
  • the embodiments of the present invention also need to meet the following two requirements:
  • the characteristics of the traffic network can be known: For the parallel actual operation requirements of traffic analysis and traffic data processing, the traffic between the blocks occurs on the traffic information of the transmitting neighbors, so the number of external contacts and traffic In proportion, the number of contacts is used as the traffic indicator.
  • the calculation of each calculation unit is approximately proportional to the number of vehicles to be analyzed by the unit. However, since the number of vehicles is a dynamic information, it is sometimes possible to approximate the number of vehicles as the calculation index by using the total length of the section of the block.
  • the transportation network partitioning scheme based on grid mapping growth proposed in combination with the idea of graph growth method is simply divided into three steps: one is grid mapping, the other is growth division, and the third is mapping restoration.
  • the purpose of the mapping is to obtain a spatial distribution of traffic network rules.
  • the advantage is that the analysis speed of the growth division operation can be improved, the number of executions of the traversal search is greatly reduced, and the complexity is reduced.
  • the growth division is based on the grid weight and the node correlation degree obtained in the mapping stage, and the division result is the corresponding grid combination.
  • a method for dividing a traffic network based on grid mapping growth includes the following steps: Step 101: Perform grid mapping on the obtained traffic flow data of a specified traffic network area, and obtain weight values of each grid. The degree of association between adjacent grids.
  • Step 102 Perform grid growth division according to the weight value of each grid and the degree of association between adjacent grids, to obtain a corresponding grid combination.
  • the method further includes: after obtaining the corresponding grid combination, performing grid mapping restoration in the specified traffic network area, and restoring the grid combination to a corresponding traffic network block.
  • the method further includes: an initialization process
  • the initialization process specifically includes: performing, according to the traffic flow data of the designated traffic network area
  • the task is divided and the divided tasks are configured to different computing servers, and the number of divided blocks to be divided and processed in the designated traffic network area is determined according to the number of the computing servers.
  • the traffic flow data includes at least: length information of the side of the traffic network and topology information, and a spatial location of the intersection of the traffic network.
  • obtaining the weight value of each of the grids specifically includes: when traversing all the edges of the traffic network, accumulating the length information of the edge into the corresponding grid to obtain the calculation amount of the grid and serving as a grid Weights;
  • Obtaining the degree of association between the adjacent grids specifically includes: when traversing all the edges of the traffic network, obtaining edges crossing the grid according to the topology information, and contiguous grids corresponding to the edges of the spanning grid The degree of association is accumulated to obtain the traffic of the grid and as the degree of association between the adjacent grids.
  • the weight value of each grid and the degree of association between adjacent grids are specifically: obtaining the calculation amount of each grid, and each adjacent grid corresponding to four adjacent directions The degree of association between the grids and as the traffic of the grid;
  • the performing the grid growth division specifically includes: obtaining the first non-empty grid as a starting position of the grid growth division according to the progressive scan manner, and analyzing the growth division conditions of the adjacent adjacent grids, respectively,
  • the grid of the growth division condition is marked as a divided grid, and the calculation amount and the traffic of the grid of the divided grid are added to the divided block and used as the calculation amount and the traffic of the divided block, and the setting is performed. Divide the lateral length and longitudinal length of the block until the end of the grid growth division.
  • the non-empty grid is specifically a grid in which the calculation amount of the grid and the traffic are not zero at the same time;
  • the marking the grids that meet the growth division condition one by one as the divided grids specifically includes: A perimeter of the partitioned block adjacent to each currently scanned grid is not divided into grids, and the calculated amount and traffic of the grid of the undivided grid are calculated and added to the partitioned block.
  • the calculation amount and the traffic of the divided block if the calculated amount of the divided block is not greater than the calculated upper limit X of the divided divided block, the undivided raster is marked as the divided grid, and the grid is continued. Scan and grow divisions.
  • the server can complete the assignment of tasks between different processing capability systems.
  • a load sharing configuration server based on grid mapping growth the server comprises: a grid mapping unit and a grid growth dividing unit; wherein, the grid mapping unit is configured to grid traffic flow data of the specified traffic network area Grid mapping, obtaining the weight value of each grid and the degree of association between adjacent grids; the grid growth dividing unit is configured to perform grid growth according to the weight value of each grid and the degree of association between adjacent grids Divide, get the corresponding grid combination.
  • the server further includes: a grid mapping restoration unit configured to perform grid mapping restoration in the specified traffic network area, and restore the grid combination to a corresponding traffic network block.
  • the traffic flow data includes at least: length information of the side of the traffic network and topology information, and a spatial location of the intersection of the traffic network.
  • the grid mapping unit is further configured to: when traversing all sides of the traffic network, accumulating the length information of the edge into the corresponding grid to obtain the calculation amount of the grid and using the weight value of the grid as a weight value of the grid Obtaining an edge of the grid across the edge of the grid according to the topology information, and accumulating the correlation degree of the adjacent grid corresponding to the edge of the grid to obtain the traffic of the grid and serving as the space between the adjacent grids. Correlation.
  • the grid mapping unit is further configured to obtain the traffic of each gate after the grid mapping ends.
  • the grid growth dividing unit is further configured to acquire the first non-empty grid as a starting position of the grid growth division according to the progressive scanning manner, and analyze the growth division conditions of the adjacent adjacent grids, and the ones will conform to the
  • the grid of the growth division condition is marked as a divided grid, and the calculation amount and the traffic of the grid of the divided grid are added to the division block and used as the calculation amount and the traffic of the division block, and the division area is set. The lateral length and longitudinal length of the block until the end of the grid growth division.
  • the load sharing configuration server can be configured as a main computing server in the system, and the load balancing task is configured for each computing server that undertakes the calculation, and the data input server constitutes a processing system configured for area division and load sharing in the traffic network.
  • a complete application processing system consists of a data input server, a main computing server, more than one computing server, and a data publishing server. among them,
  • the data entry server is configured to send raw traffic stream data collected by the outfield road sensor to the host computing server.
  • the main computing server is configured to implement a partitioning operation based on the original traffic flow data and load-allocate the respective computing servers based on the results of the partitioning operations.
  • the partitioning operation based on the raster map growth is configured on the main computing server responsible for load balancing allocation among the respective computing servers.
  • the computing server is configured to analyze and calculate the traffic flow data in each block of the finally divided traffic network according to the allocation of the main computing server, and obtain the road congestion condition and send it to the data publishing server. In addition to the necessary data transfer between the various computing servers, they are independently operated.
  • the data distribution server is configured to publish and transmit congestion information in each of the blocks of the finally divided traffic network to the user's mobile terminal.
  • the main computing server when the main computing server initially acquires the collected original traffic flow data from the data input server, firstly, the workload is determined according to the data situation, and the task is split according to the workload, by each Existing computing servers each load a single task.
  • the core of the city is the road network.
  • the urban congestion information will be based on the road segment as the basic analysis unit, and the congestion status will be obtained by calculating each road segment. Therefore, the splitting of the road network is closely related to the load balancing of each computing server.
  • the main computing server first determines the number of blocks that the current traffic network will be split according to the number of computing servers. The number of each computing server can be taken as the number of split blocks, and then the current traffic network is mapped by the partition based on the growth of the raster map. The following describes the partitioning process based on the raster map growth.
  • the partitioning process based on grid mapping growth including grid mapping, growth partitioning and restoration operations.
  • Grid mapping The original traffic flow data of a traffic network contains the spatial location of the intersection, the length of the edge, and the topology information. In terms of distribution, the location and connection relationship of the intersection are spatially irregular.
  • the main purpose of the raster mapping operation is to obtain the spatial distribution of the rules to achieve the growth side. Reasonable judgment of direction, while avoiding repeated iterative operations of spatial position analysis, improving execution efficiency.
  • the grid mapping is specifically:
  • Grid generation for a road traffic network in a given area First, create a circumscribed rectangle of the traffic network, and then divide the rectangle into a rectangular rectangle.
  • the number of divided grids is determined according to the accuracy requirements. Assuming that n blocks are required to be divided according to the above task division, the minimum number of grids should not be less than 100 or 10 ⁇ 2 , otherwise the division result is too rough. It is required that the shape of each grid should be square, so the number of rows and columns may be different. In this way, the graphics of the rules as far as possible are divided, so that the rules or irregularities of the traffic network can be uniformly handled.
  • Grid mapping traverse all sides of the traffic network, accumulate the length information of the edge into its corresponding grid as the calculation amount of the grid; when traversing all edges, for the edges across the grid, at the same time The degree of association of the two adjacent grids corresponding to the edges of the grid is accumulated. Each grid stores the association degree of the four adjacent grids corresponding to the top, bottom, left, and right, and the degree of association is the traffic of the grid.
  • FIG. 2 shows the schematic diagram of the raster mapping process.
  • the road network of the main city of a city is divided according to the division scheme of the embodiment of the present invention, and it is assumed that there are three calculation servers according to the task, and the division is required according to the number of calculation servers.
  • the aspect ratio of the original image is 2119 ⁇ 1953, which is divided according to the calculation rule of generating 10 grids by the short side.
  • 10 grids are generated vertically, 11 grids are generated horizontally, and a grid 110 is generated.
  • the mapping operation the calculation amount of each grid and the traffic corresponding to the grid in four directions are obtained.
  • the grid identifier is empty, that is, an empty grid. For example, first take out the grid with coordinates (1, 1). If it is empty, then take out the grid of (1, 2). If it is still empty, continue to find the grid with coordinates (1, 3). If you have reached the end of the line, then remove the grid of the first column of the next row. Mark the removed raster as the current raster and mark the raster as a divided raster. At the same time, the calculation amount and the traffic of the grid are added to the calculation amount and the traffic of the divided block, and the horizontal length and the vertical length of the block are set.
  • the growth division condition is: the block calculation amount is not greater than the X candidate position, and the block calculation amount is not greater than the X candidate position to be analyzed, and the candidate position with the least traffic is selected. This rule guarantees that the generated block traffic is as low as possible.
  • the grid of the location is divided into blocks, labeled as divided grids. Adjust the calculation amount, traffic, lateral length, and vertical length of the block. Go to the next step to grow.
  • the growth division process is illustrated by taking the growth of the urban main city traffic network as shown in Figure 3.
  • the first non-empty grid is (1, 5); the second block is divided from (5, 5). . Since three blocks need to be divided, two blocks are calculated, and the remaining non-empty grids constitute the third block.
  • the raster restoration process is relatively simple, and only the actual road traffic network corresponding to each divided block needs to be taken out. Since the topology of the initial road traffic network is set when undivided, the topology of each block is separately set when fetching, and the side settings for communication with other blocks are required. Block number information.
  • a complete urban transportation network will be divided into multiple blocks, and the main computing server will evenly distribute the blocks to each computing server, ie: for the above initialization process, based on task partitioning
  • Each of the computing servers is loaded with a block data analysis operation. Since each computing server will calculate the congestion of the traffic flow based only on the part of the road segment to which it is loaded, and the analysis operation of the data volume of the block is the final divided block obtained by the above-mentioned partitioning process based on the raster mapping growth. Data volume load balancing is based on, therefore, the need for load balancing between computing servers is met.
  • the spatial distribution of the regular traffic network can be obtained by grid mapping, and then the grid growth is divided according to the comprehensive consideration of the weight value of each grid and the correlation degree between adjacent grids.
  • the resulting partitions on the spatial distribution are necessarily load balanced.
  • the execution complexity is low, the precision is high, the load is balanced, and the parallel processing is efficient.

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Abstract

本发明公开了一种基于栅格映射生长的交通网络划分方法及负载分担配置服务器，其中，该方法包括对获取的指定交通网络区域的交通流数据进行栅格映射，得到各栅格的权重值和相邻栅格间的关联度；根据所述各栅格的权重值和相邻栅格间的关联度进行栅格生长划分，得到相对应的栅格组合。该服务器中的栅格映射单元用于对获取的指定交通网络区域的交通流数据进行栅格映射，得到各栅格的权重值和相邻栅格间的关联度；栅格生长划分单元用于根据所述各栅格的权重值和相邻栅格间的关联度进行栅格生长划分，得到相对应的栅格组合。采用本发明，执行复杂度低、精度高、负载均衡、并行处理的效率高。

Description

一种基于栅格映射生长的交通网络划分方法及配置服务器 技术领域
本发明属于智能交通领域， 尤其涉及一种基于栅格映射生长的交通网络划 分方法及负载分担配置服务器。 背景技术
随着智能交通系统的发展， 动态路径诱导、 出行者信息服务、 动态交通流 组织等先进的管理手段和理念不断发展， 分析效率是实时分析和交通管理关心 的问题。
在进行交通分析时需要将整个城市的交通网络分拆为若干个部分， 每个部 分的运算由一个处理器负责，每个处理器只处理自己负责部分网络的车辆情况。 一个好并行执行系统应该使各个处理器的负载平衡， 并且相互之间的数据传递 尽可能少。
在现有的交通网络划分方案中， 研究分为几类：
1 )基于分割的划分方法。 其划分结果受初始结点选择的影响很大， 不能一 次划分就得到最终的划分结果， 有反复迭代的调整的过程。
2 )基于合并的划分方法。很多时候通过直接分割得到的分割结果不够理想， 而且执行效率也不高， 就出现了合并的思路， 不过合并通常也不能一次就得到 最终的划分结果， 需要多次合并才行。
3 )基于合并和分割的组合划分方法。也存在上述反复迭代的调整的过程和 多次合并的问题， 精确度有所提高， 但是执行复杂度高， 划分结果也不尽理想。
综上所述， 现有交通网络划分方案存在的问题是： 1 )无论哪种方案， 都不 能一次划分就得到最终的划分结果， 需要反复迭代的调整及多层合并才能够得 到， 因此， 执行复杂度高。 2 )为了降低复杂度， 使运算不要过于复杂， 目前通 常针对普通的矩阵情形， 由于是有所选择的， 因此， 在交通网络划分中的适用 范围不大。 3 )现有的交通网络划分方案中， 由于划分的精度不高， 进行划分运 算的每个处理器的负载就不够均衡， 并行处理的效率不高， 当执行复杂度高的 情况下问题就更为明显了。 目前迫切需要一种执行复杂度低、 精度高、 负载均 衡、 并行处理效果高的解决方案。 发明内容
有鉴于此， 本发明实施例的主要目的在于提供一种基于栅格映射生长的交 通网络划分方法及负载分担配置服务器， 执行复杂度低、 精度高、 负载均衡、 并行处理的效率高。
为达到上述目的， 本发明实施例的技术方案是这样实现的：
一种基于栅格映射生长的交通网络划分方法， 该方法包括：
对获取的指定交通网络区域的交通流数据进行栅格映射， 得到各栅格的权 重值和相邻栅格间的关联度；
根据所述各栅格的权重值和相邻栅格间的关联度进行栅格生长划分， 得到 相对应的栅格组合。
其中， 该方法还包括： 得到相对应的栅格组合后， 在所述指定交通网络区 域中进行栅格映射还原， 将所述栅格组合还原成对应的交通网络区块。
其中， 所述对获取的指定交通网络区域的交通流数据进行栅格映射之前， 该方法还包括： 初始化过程；
所述初始化过程具体包括： 根据指定交通网络区域的所述交通流数据进行 任务划分并将划分的任务配置到不同的计算服务器， 根据所述计算服务器的数 量确定所述指定交通网络区域中待划分处理的划分区块数量。
其中， 所述交通流数据至少包括： 交通网络的边的长度信息和拓朴信息、 交通网络交叉口的空间位置。
其中， 得到所述各栅格的权重值具体包括： 遍历交通网络的所有边时， 将 所述边的长度信息累加至其所对应的栅格中得到栅格的计算量并作为栅格的权 重值； 得到所述相邻栅格间的关联度具体包括： 遍历交通网络的所有边时， 根据 所述拓朴信息得到跨越栅格的边， 将所述跨越栅格的边所对应的相邻栅格的关 联度进行累加得到栅格的通信量并作为所述相邻栅格间的关联度。
其中，栅格映射结束后得到各栅格的权重值和相邻栅格间的关联度具体为： 得到每个栅格的计算量、 和每个栅格对应四个相邻方向的相邻栅格间的关联度 并作为栅格的通信量；
所述进行栅格生长划分具体包括： 按照逐行扫描的方式获取到第一个非空 栅格作为栅格生长划分的起始位置， 分析其周边相邻栅格的生长划分条件， 逐 一将符合所述生长划分条件的栅格标记为已划分栅格， 将所述已划分栅格的栅 格的计算量和通信量加入到划分区块中并作为划分区块的计算量和通信量， 设 置划分区块的横向长度和纵向长度， 直至结束栅格生长划分。
其中， 所述非空栅格具体为栅格的计算量和通信量不同时为零的栅格； 所述逐一将符合所述生长划分条件的栅格标记为已划分栅格具体包括： 将 所述划分区块中与每一个当前扫描到的栅格相邻的周边未划分栅格， 作为待选 位置， 计算未划分栅格的栅格的计算量和通信量并加入到划分区块中作为划分 区块的计算量和通信量， 若划分区块的计算量不大于设置的划分区块的计算量 上限 X， 则将所述未划分栅格标记为已划分栅格， 继续对栅格进行扫描和生长 划分。
一种基于栅格映射生长的负载分担配置服务器， 该服务器包括： 栅格映射 单元、 栅格生长划分单元； 其中，
所述栅格映射单元， 配置为对获取的指定交通网络区域的交通流数据进行 栅格映射， 得到各栅格的权重值和相邻栅格间的关联度；
所述栅格生长划分单元， 配置为根据所述各栅格的权重值和相邻栅格间的 关联度进行栅格生长划分， 得到相对应的栅格组合。
其中， 该服务器还包括： 栅格映射还原单元， 配置为在所述指定交通网络 区域中进行栅格映射还原， 将所述栅格组合还原成对应的交通网络区块。
其中， 所述交通流数据至少包括： 交通网络的边的长度信息和拓朴信息、 交通网络交叉口的空间位置。
其中， 所述栅格映射单元， 进一步配置为遍历交通网络的所有边时， 将所 述边的长度信息累加至其所对应的栅格中得到栅格的计算量并作为栅格的权重 值， 根据所述拓朴信息得到跨越栅格的边， 将所述跨越栅格的边所对应的相邻 栅格的关联度进行累加得到栅格的通信量并作为所述相邻栅格间的关联度。
其中， 所述栅格映射单元， 进一步配置为栅格映射结束后得到每个栅格的 信量；
所述栅格生长划分单元， 进一步配置为按照逐行扫描的方式获取到第一个 非空栅格作为栅格生长划分的起始位置，分析其周边相邻栅格的生长划分条件， 逐一将符合所述生长划分条件的栅格标记为已划分栅格， 将所述已划分栅格的 栅格的计算量和通信量加入到划分区块中并作为划分区块的计算量和通信量， 设置划分区块的横向长度和纵向长度， 直至结束栅格生长划分。
本发明实施例对获取的指定交通网络区域的交通流数据进行栅格映射， 得 到各栅格的权重值和相邻栅格间的关联度； 根据所述各栅格的权重值和相邻栅 格间的关联度进行栅格生长划分， 得到相对应的栅格组合。
由于通过栅格映射能得到规则的交通网络的空间分布， 则在该空间分布上 根据各栅格的权重值和相邻栅格间的关联度的综合考虑进行栅格生长划分后， 得到的该空间分布上的各个划分区块必然是负载均衡的。 采用本发明实施例， 执行复杂度低、 精度高、 负载均衡、 并行处理的效率高。 附图说明
图 1为本发明实施例方法原理的实现流程示意图；
图 2为本发明实施例的栅格映射过程示意图；
图 3为本发明实施例主城区道路交通网络的栅格映射示意图；
图 4为本发明实施例栅格生长划分的实现流程示意图； 图 6a 为本 W '最终划分 区块 1的示意图
'最终划分区块 2 的示意图
图 6c为本发 f最终划分区块 3 的示意图。 具体实施方式
本发明适用范围广泛， 不仅适用于规则的、 普通的矩阵规划情形， 也适用 于具有稀疏矩阵特点、 不规则的交通网络。
稀疏矩阵的特点往往也有能找出规律的地方， 比如结点往往是和在空间上 临近的结点有连接， 而且一个城市的交通网络虽然属于不规则网络， 但是多数 情况并不会是完全不规则的图形， 从城市发展演变的过程看， 城市交通网络往 往属于比较规则的情况。 本发明实施例的这种基于栅格映射生长的交通网络划 分方案， 就是基于这样的思路产生的， 通过分析交通网络的特点， 找出规律， 并运用到本发明实施例的划分方案中。本发明实施例是根据交通网络的的特点， 结合图形生长方法的思路提出的方案， 划分原则为： 给定一个交通网络， 可以 任意划分为 n个区块， 区块的划分灵活； 区块之间的总通信量要控制得尽可能 少， 每个区块的计算量要尽可能均等， 从而能确保多个处理器在计算数据时的 负载均衡， 提高并行处理效率。 而且， 该方案一次划分完成就能得到最终划分 结果， 既不需要反复迭代的调整阶段， 也不需要进行多层合并， 因此， 无需反 复调整、 一步到位， 简化计算， 从而精度高， 执行效率也高。
本发明实施例除了满足交通网络划分高效的要求外， 还需要满足如下的二 个要求：
1、尽可能保证分割的网络联系要少， 也就是暴露在周围的边要少， 这样可 以减少通信次数和数据总量， 降低延时。
2、 尽可能保证分割的区域的计算需求大小和对应的处理器计算能力成正 比， 这样可以充分发挥各个处理器的计算能力， 减少负载不平衡造成的相互等 待， 避免并行处理的效率受到影响。
综上所述， 分析交通网络的特点可知： 针对交通分析与交通数据处理并行 实际运算需求， 区块之间的通信量发生在传递邻边的流量信息上， 因此对外的 联系边数与通信量成正比， 由联系边数作为通信量指标。 每个计算单元的计算 量近似和该单元有待分析的车数成正比， 但是由于车数是一个动态信息， 有时 也可以用区块的路段总长度近似代替车数作为计算量指标。
结合图形生长方法的思路提出的基于栅格映射生长的交通网络划分方案， 简单来说， 分为三步： 一是栅格映射， 二是生长划分， 三是映射还原。 1 )进行 栅格映射， 这个操作类似于合并过程， 但是其不只包含合并， 还同时包含展开， 映射的目的是为了获得一个交通网络规则的空间分布。 其好处是可以提高生长 划分操作的分析速度， 大大减少遍历搜索的执行次数， 降低复杂度。 2 )在栅格 映射的基础上进行生长划分， 该操作的依据为映射阶段获得的栅格权重和节点 关联度， 其划分结果为相应的栅格组合。 3 )进行映射还原， 将栅格组合还原为 交通网络的区块， 划分完成。
上述说明是对本发明基本思路的阐述， 以便于理解本发明。 以下对本发明 方案的具体实现进行描述。
一种基于栅格映射生长的交通网络划分方法，如图 1所示， 包括以下步骤： 步骤 101、 对获取的指定交通网络区域的交通流数据进行栅格映射， 得到 各栅格的权重值和相邻栅格间的关联度。
步骤 102、根据各栅格的权重值和相邻栅格间的关联度进行栅格生长划分， 得到相对应的栅格组合。
进一步的， 该方法还包括： 得到相对应的栅格组合后， 在所述指定交通网 络区域中进行栅格映射还原， 将所述栅格组合还原成对应的交通网络区块。
进一步的， 所述对获取的指定交通网络区域的交通流数据进行栅格映射之 前， 该方法还包括： 初始化过程；
所述初始化过程具体包括： 根据指定交通网络区域的所述交通流数据进行 任务划分并将划分的任务配置到不同的计算服务器， 根据所述计算服务器的数 量确定所述指定交通网络区域中待划分处理的划分区块数量。
进一步的， 所述交通流数据至少包括： 交通网络的边的长度信息和拓朴信 息、 交通网络交叉口的空间位置。
进一步的，得到所述各栅格的权重值具体包括： 遍历交通网络的所有边时， 将所述边的长度信息累加至其所对应的栅格中得到栅格的计算量并作为栅格的 权重值；
得到所述相邻栅格间的关联度具体包括： 遍历交通网络的所有边时， 根据 所述拓朴信息得到跨越栅格的边， 将所述跨越栅格的边所对应的相邻栅格的关 联度进行累加得到栅格的通信量并作为所述相邻栅格间的关联度。
进一步的， 栅格映射结束后得到各栅格的权重值和相邻栅格间的关联度具 体为： 得到每个栅格的计算量、 和每个栅格对应四个相邻方向的相邻栅格间的 关联度并作为栅格的通信量；
所述进行栅格生长划分具体包括： 按照逐行扫描的方式获取到第一个非空 栅格作为栅格生长划分的起始位置， 分析其周边相邻栅格的生长划分条件， 逐 一将符合所述生长划分条件的栅格标记为已划分栅格， 将所述已划分栅格的栅 格的计算量和通信量加入到划分区块中并作为划分区块的计算量和通信量， 设 置划分区块的横向长度和纵向长度， 直至结束栅格生长划分。
进一步的，所述非空栅格具体为栅格的计算量和通信量不同时为零的栅格； 所述逐一将符合所述生长划分条件的栅格标记为已划分栅格具体包括： 将 所述划分区块中与每一个当前扫描到的栅格相邻的周边未划分栅格， 作为待选 位置， 计算未划分栅格的栅格的计算量和通信量并加入到划分区块中作为划分 区块的计算量和通信量， 若划分区块的计算量不大于设置的划分区块的计算量 上限 X， 则将所述未划分栅格标记为已划分栅格， 继续对栅格进行扫描和生长 划分。
对于异构系统， 只要设置不同的上限值 X， 服务器即可完成不同处理能力 系统之间任务的分配。 一种基于栅格映射生长的负载分担配置服务器， 该服务器包括： 栅格映射 单元、 栅格生长划分单元； 其中， 栅格映射单元， 配置为对获取的指定交通网 络区域的交通流数据进行栅格映射， 得到各栅格的权重值和相邻栅格间的关联 度； 栅格生长划分单元， 配置为根据所述各栅格的权重值和相邻栅格间的关联 度进行栅格生长划分， 得到相对应的栅格组合。
进一步的， 该服务器还包括： 栅格映射还原单元， 配置为在所述指定交通 网络区域中进行栅格映射还原， 将所述栅格组合还原成对应的交通网络区块。
进一步的， 所述交通流数据至少包括： 交通网络的边的长度信息和拓朴信 息、 交通网络交叉口的空间位置。
进一步的， 所述栅格映射单元， 进一步配置为遍历交通网络的所有边时， 将所述边的长度信息累加至其所对应的栅格中得到栅格的计算量并作为栅格的 权重值， 根据所述拓朴信息得到跨越栅格的边， 将所述跨越栅格的边所对应的 相邻栅格的关联度进行累加得到栅格的通信量并作为所述相邻栅格间的关联 度。
进一步的， 所述栅格映射单元， 进一步配置为栅格映射结束后得到每个栅 的通信量。 栅格生长划分单元进一步配置为按照逐行扫描的方式获取到第一个 非空栅格作为栅格生长划分的起始位置，分析其周边相邻栅格的生长划分条件， 逐一将符合所述生长划分条件的栅格标记为已划分栅格， 将所述已划分栅格的 栅格的计算量和通信量加入到划分区块中并作为划分区块的计算量和通信量， 设置划分区块的横向长度和纵向长度， 直至结束栅格生长划分。
上述负载分担配置服务器可以作为系统中的主计算服务器， 与将负载均衡 的任务配置给承担运算的各个计算服务器， 数据输入服务器等构成各种配置为 交通网络中区域划分和负载分担的处理系统。
下面结合附图对本发明的实施作进一步的详细描述。
应用实例： 以动态交通拥堵状态的导航应用为例， 出行者将在移动终端上 接收和查看当前时刻城市道路的拥堵情况。 这个拥堵信息依赖后台的应用处理 系统进行发布。 包括初始化过程和基于栅格映射生长的划分过程。
一、 初始化过程， 需完成相应的设置。
一个完整的应用处理系统包含数据输入服务器、 主计算服务器、 超过一台 的计算服务器、 数据发布服务器。 其中，
数据输入服务器配置为将由外场道路传感器采集的原始交通流数据发送给 主计算服务器。
主计算服务器配置为根据原始交通流数据实现划分运算并基于划分运算的 结果对各个计算服务器进行负载均衡分配。 基于栅格映射生长的划分运算被配 置在负责各个计算服务器间进行负载均衡分配的该主计算服务器上。
计算服务器配置为按照主计算服务器的分配， 对最终划分好的交通网络的 各个区块内的交通流数据进行分析运算， 得到道路拥堵的情况并发送给数据发 布服务器。 各个计算服务器间除了必要的数据传递， 各自独立运算。
数据发布服务器配置为将最终划分好的交通网络的各个区块内的拥堵信息 进行发布并发送到用户的移动终端上。
这里， 针对主计算服务器具体来说， 当主计算服务器最初从数据输入服务 器获取到所采集的原始交通流数据时， 首先要根据数据情况判断工作量， 并根 据工作量进行任务拆分， 由每一个已有的计算服务器各自负载一块任务。
在所述任务拆分时， 最核心要拆分的就是城市的道路网， 城市拥堵信息将 以路段为基本分析单位， 通过对每一个路段的计算获取拥堵状态。 所以道路网 的拆分对各计算服务器的负载均衡情况关系密切， 主计算服务器会依据各计算 服务器的数量， 首先来确认当前交通网络将要被拆分的区块数量。 可以取各计 算服务器的数量作为拆分的区块数量， 然后利用基于栅格映射生长的划分来对 当前交通网络进行地图拆分， 下面描述基于栅格映射生长的划分过程。
二、基于栅格映射生长的划分过程， 包括栅格映射、 生长划分和还原操作。
1、 栅格映射： 一个交通网络的原始交通流数据中包含交叉口的空间位置、 边的长度和拓朴信息。 在分布上， 交叉口的位置和连接关系在空间上是不规则 的。 栅格映射操作的最主要目的是为了获得规则的空间分布， 以实现对生长方 向的合理判断， 同时避免空间位置分析的反复迭代操作， 提高执行效率。
栅格映射具体为：
①对给定区域的道路交通网络进行栅格生成： 首先生成一个交通网络的外 接矩形， 然后对外接矩形进行栅格划分。 划分的栅格数量根据精度要求来决定， 假定根据上述任务划分得到要求划分好 n个区块， 则最少栅格数量也不应低于 100或 10η2个， 否则划分结果过于粗糙。 要求每一个栅格的形状应为正方形， 因此可能行和列的栅格数量可能不同。 如此一来， 就是针对尽量规则的图形进 行划分了， 从而能统一处理交通网络规则或不规则的情况。
②进行栅格映射： 遍历交通网络的所有边， 将边的长度信息累加至其所对 应的栅格中作为栅格的计算量； 对所有边进行遍历时， 对于跨越栅格的边， 同 时将该跨越栅格的边所对应的两个相邻栅格的关联度进行累加。 每个栅格分别 存储对应上、 下、 左、 右四个相邻栅格的关联度， 这个关联度即为栅格的通信 量。
这里， 如果相邻的两个栅格之间没有联系， 那么这两个栅格的关联度就为 0， 没有通信量。 位于整体图形边缘的栅格必然有一个方向其通信量为 0， 如果 一个栅格中没有路段通过， 那么该栅格的计算量和通信量同时为 0， 该栅格标 识为空。 如图 2所示为栅格映射过程示意图。
如图 3所示， 结合实际交通网络划分， 对一城市主城区的道路网络基于本 发明实施例的划分方案进行划分操作， 假定根据任务划分有 3个计算服务器， 则根据计算服务器的数量需要划分三个区块， 原图的横纵比为 2119 χ 1953， 按 短边生成 10个栅格的计算规则进行划分， 则纵向生成栅格 10个， 横向生成栅 格 11个， 一共生成栅格 110个。 然后通过映射操作获得每个栅格的计算量和栅 格对应 4个方向的通信量。
2、 栅格生长划分：
假定要求划分的区块数量 n=k， 每个区块的计算量为 X， 则按照如下规则 生长， 算法流程如图 4所示：
①按照逐行的方式扫描栅格， 取出一个非空的栅格作为生长划分的起始位 置（非空的栅格相对于空的栅格而言， 当一栅格的计算量和通信量同时为 0时， 该栅格标识为空， 即空的栅格）。 比如先取出坐标为（1， 1 )的栅格， 如果为空， 则接着取出 （1， 2 )的栅格， 如果仍为空， 则继续寻找坐标为 （1， 3 )的栅格。 如果已经到了该行的结尾， 则接着取出下一行第一列的栅格。 将取出的栅格标 为当前栅格， 标记该栅格为已划分栅格。 同时将该栅格的计算量和通信量加入 到划分区块的计算量和通信量中， 并设置区块的横向长度和纵向长度。
②逐一分析与当前区块中每一个栅格相邻的周边未划分栅格作为待选位 置， 计算出分别加入每一个相邻栅格后， 区块的计算量、 通信量、 横向长度和 纵向长度。
③生长划分条件为： 区块计算量不大于 X的待选位置， 对区块计算量不大 于 X的待选位置进行分析， 选出通信量最少的待选位置。 这条规则保证生成的 区块通信量尽可能低。
④如果有多个位置的通信量相同， 选出令横向长度和纵向长度更接近的位 置。 这条规则保证选择的区块尽可能面积周长比大。
⑤如果仍有多个位置满足生长划分条件， 则选出行值更低的位置。 这条规 则保证方法的唯一性。
⑥如果仍有多个位置满足生长划分条件， 则选出列值更低的位置。 这条规 则保证方法的唯一性。
⑦如果所有位置的栅格都为空或者已被划分， 则说明整个区块的周围都不 具备生长划分条件， 则根据上述①的规则找到一个没有被划分的栅格， 重新开 始生成过程。这条规则保证当道路网不规则时，一个区块包含多个岛的可能性。
⑧确定好生长划分的位置后， 将该位置的栅格划入区块， 标记为已划分栅 格。 调整区块的计算量、 通信量、 横向长度和纵向长度。 进入下一步生长。
⑨如果所有位置的计算量都大于 X， 那么生长结束， 该区块划分完成。 前面的描述中， 出于便于理解和说明的角度考虑， 假定每个区块的计算量 相同，但对异构系统来说，每个计算单元计算能力可能不同，假定分别为： （vl， v2 , ... ... vn )。 这时候， 本发明实施例的方案可以简单的通过调整生长停止条件 来满足异构系统的要求。
以图 3所示的城市主城区交通网络生长划分为例来说明生长划分过程。 如 图 5所示， 从实际情况看， 第一个区块的划分中， 第一个不为空的栅格为 （1， 5 ); 第二个区块的划分从（5， 5 )开始。 因为需要划分三个区块， 所以计算出 两个区块， 剩余不为空的栅格组成第三个区块。
3、 栅格映射还原：
栅格还原过程较为简单， 只需要将每个划分好的区块对应的实际道路交通 网络取出即可。 由于初始的道路交通网络的拓朴结构是未分割时设置好的， 所 以在取出时要对每个区块的拓朴结构进行单独设置， 同时对需要与其他区块进 行通信的边设置对应的区块编号信息。
在栅格映射时， 不可避免的存在栅格切割路段的情形。 因此生成的区块就 会存在拆分路段的情形。在实际应用中，有些情况对于是否拆分路段没有要求， 比如在进行动态交通分配仿真时， 相邻的区域只需要交换每次步长流入和流出 的车辆信息， 因此在路段的任何位置划分为不同的区块都是可以的。 但有时的 应用需求要求路段必须连续， 这时在本发明实施例的方案中， 在进行栅格还原 时，就需要将拆分的路段合并然后划分到一个区块中。栅格还原后结果如图 6a、 6b、 6c所示， 分别对应最终划分区块 1、 2、 3的示意图。
最终， 拆分完成后， 一个完整的城市交通网络将被分割成多个区块， 主计 算服务器把区块平均分配到每一个计算服务器上， 即： 对上述初始化过程中， 基于任务划分进行配载的各个计算服务器， 进行区块数据量分析运算的配载。 由于每个计算服务器都将只基于被配载到的那部分路段计算交通流的拥堵情 况， 而且该区块数据量的分析运算是上述基于栅格映射生长的划分过程得到的 最终划分区块间数据量负载均衡为依据的， 因此， 满足了各个计算服务器间负 载均衡的需求。
以上所述为本发明的较佳实施例而已， 并非用于限定本发明的保护范围。 工业实用性
本发明实施例通过栅格映射能得到规则的交通网络的空间分布， 则在 该空间分布上根据各栅格的权重值和相邻栅格间的关联度的综合考虑进行 栅格生长划分后， 得到的该空间分布上的各个划分区块必然是负载均衡的。 采用本发明实施例， 执行复杂度低、 精度高、 负载均衡、 并行处理的效率 高。

Claims

权利要求书
1、 一种基于栅格映射生长的交通网络划分方法， 该方法包括： 对获取的指定交通网络区域的交通流数据进行栅格映射， 得到各栅格 的权重值和相邻栅格间的关联度；
根据所述各栅格的权重值和相邻栅格间的关联度进行栅格生长划分， 得到相对应的栅格组合。
2、 根据权利要求 1所述的方法， 其中， 该方法还包括： 得到相对应的 栅格组合后， 在所述指定交通网络区域中进行栅格映射还原， 将所述栅格 组合还原成对应的交通网络区块。
3、 根据权利要求 1所述的方法， 其中， 所述对获取的指定交通网络区 域的交通流数据进行栅格映射之前， 该方法还包括： 初始化过程；
所述初始化过程具体包括： 根据指定交通网络区域的所述交通流数据 进行任务划分并将划分的任务配置到不同的计算服务器， 根据所述计算服 务器的数量确定所述指定交通网络区域中待划分处理的划分区块数量。
4、 根据权利要求 1、 2或 3所述的方法， 其中， 所述交通流数据至少 包括： 交通网络的边的长度信息和拓朴信息、 交通网络交叉口的空间位置。
5、 根据权利要求 4所述的方法， 其中， 得到所述各栅格的权重值具体 包括： 遍历交通网络的所有边时， 将所述边的长度信息累加至其所对应的 栅格中得到栅格的计算量并作为栅格的权重值；
得到所述相邻栅格间的关联度具体包括： 遍历交通网络的所有边时， 根据所述拓朴信息得到跨越栅格的边， 将所述跨越栅格的边所对应的相邻 栅格的关联度进行累加得到栅格的通信量并作为所述相邻栅格间的关联 度。
6、 根据权利要求 4所述的方法， 其中， 栅格映射结束后得到各栅格的 权重值和相邻栅格间的关联度具体为： 得到每个栅格的计算量、 和每个栅 所述进行栅格生长划分具体包括： 按照逐行扫描的方式获取到第一个 非空栅格作为栅格生长划分的起始位置， 分析其周边相邻栅格的生长划分 条件， 逐一将符合所述生长划分条件的栅格标记为已划分栅格， 将所述已 划分栅格的栅格的计算量和通信量加入到划分区块中并作为划分区块的计 算量和通信量， 设置划分区块的横向长度和纵向长度， 直至结束栅格生长 划分。
7、 根据权利要求 6所述的方法， 其中， 所述非空栅格具体为栅格的计 算量和通信量不同时为零的栅格；
所述逐一将符合所述生长划分条件的栅格标记为已划分栅格具体包 括： 将所述划分区块中与每一个当前扫描到的栅格相邻的周边未划分栅格， 作为待选位置， 计算未划分栅格的栅格的计算量和通信量并加入到划分区 块中作为划分区块的计算量和通信量， 若划分区块的计算量不大于设置的 划分区块的计算量上限 X， 则将所述未划分栅格标记为已划分栅格， 继续 对栅格进行扫描和生长划分。
8、 一种基于栅格映射生长的负载分担配置服务器， 该服务器包括： 栅 格映射单元、 栅格生长划分单元； 其中，
所述栅格映射单元， 配置为对获取的指定交通网络区域的交通流数据 进行栅格映射， 得到各栅格的权重值和相邻栅格间的关联度；
所述栅格生长划分单元， 配置为根据所述各栅格的权重值和相邻栅格 间的关联度进行栅格生长划分， 得到相对应的栅格组合。
9、 根据权利要求 8所述的服务器， 其中， 该服务器还包括： 栅格映射 还原单元， 配置为在所述指定交通网络区域中进行栅格映射还原， 将所述 栅格组合还原成对应的交通网络区块。
10、 根据权利要求 8或 9所述的服务器， 其中， 所述交通流数据至少 包括： 交通网络的边的长度信息和拓朴信息、 交通网络交叉口的空间位置。
11、 根据权利要求 10所述的服务器， 其中， 所述栅格映射单元， 进一 步配置为遍历交通网络的所有边时， 将所述边的长度信息累加至其所对应 的栅格中得到栅格的计算量并作为栅格的权重值， 根据所述拓朴信息得到 得到栅格的通信量并作为所述相邻栅格间的关联度。
12、 根据权利要求 10所述的服务器， 其中， 所述栅格映射单元， 进一 步配置为栅格映射结束后得到每个栅格的计算量、 和每个栅格对应四个相 邻方向的相邻栅格间的关联度并作为栅格的通信量；
所述栅格生长划分单元， 进一步配置为按照逐行扫描的方式获取到第 一个非空栅格作为栅格生长划分的起始位置， 分析其周边相邻栅格的生长 划分条件， 逐一将符合所述生长划分条件的栅格标记为已划分栅格， 将所 述已划分栅格的栅格的计算量和通信量加入到划分区块中并作为划分区块 的计算量和通信量， 设置划分区块的横向长度和纵向长度， 直至结束栅格 生长划分。
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