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Distributed map matching method for massive historical floating car data

A floating car data and map matching technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of slow calculation speed and large time consumption

Inactive Publication Date: 2014-12-03
ENJOYOR COMPANY LIMITED
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AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of slow calculation speed and high time consumption in the prior art for map matching of massive data, the present invention provides a distributed map matching method based on Hadoop, which realizes fast cleaning of original data and fast calculation speed , Distributed map matching method for massive historical floating car data with less time consumption

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  • Distributed map matching method for massive historical floating car data
  • Distributed map matching method for massive historical floating car data
  • Distributed map matching method for massive historical floating car data

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[0137] Example: Using the records of 11.4 billion floating cars in Hangzhou from June 2012 to June 2013 as the data to be matched, a distributed computing environment for map matching is constructed by 1 masterPC and 100 SlavePCs. Each PC The basic configuration of the machine is 512M memory, Pentium P4C processor, about 11 billion after data cleaning and elimination, the matching time is about 21 hours, and it has also reached a high longitude.

[0138] Such as figure 1 As shown, the present invention provides a Hadoop-based distributed map matching method. The specific implementation process is as follows:

[0139] Step (1): Upload massive floating car data to the Hadoop distributed file system HDFS;

[0140] Step (2): Data preprocessing is performed through Map-Reduce. The Map function reads a row of floating car data from the HDFS file system and assigns the value to the value, the original data Figure 4 Shown.

[0141] Then call the split() method to extract each data item, and ...

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Abstract

The invention discloses a distributed map matching method for massive historical floating car data. The method comprises the following steps: (1) uploading massive floating car data to an HDFS (Hadoop Distributed File System); (2) performing distributed data cleaning; (3) when road node information is stored on each Slave child node through a distributed caching method, establishing a grid index for the road node information before any Job is executed on the nodes; (4) reading the cleaned floating car data from the HDFS through a Map-Reduce frame of Hadoop, slicing the floating car data into a plurality of data blocks by taking 128M as a unit, distributing the data blocks to the Map-Reduce of each node, and reading the road node information in a distributed cache for map matching operation, so that distributed operation is realized; (5) storing the matched information into the HDFS through a Reduce function. The distributed map matching method for the massive historical floating car data has the advantages that the calculation speed is higher, and the time consumption is lower.

Description

Technical field [0001] The invention belongs to the field of massive data processing and calculation and the field of intelligent transportation, and specifically relates to a distributed map matching method. Background technique [0002] Floating car data is one of the most important traffic data. Its output can not only provide real-time road traffic information for relevant departments, but also provide a basis for quantitative data analysis for road construction planning, congestion relief and other tasks. Map matching technology is one of the most critical items in floating car data processing. Only by judging which road the vehicle is driving on, can GPS data be converted into effective road traffic status information. [0003] Large amounts of historical data are often stored in the floating car database. The map matching of these massive historical floating car data is a prerequisite for various data processing and analysis tasks such as temporal and spatial correlation mi...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/217G06F16/182G06F16/2228
Inventor 薛益赵李建元钱涛倪升华李丹陈涛王浩
Owner ENJOYOR COMPANY LIMITED
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