Vehicle congestion degree acquiring method based on Hadoop

Abstract

The invention discloses a vehicle congestion degree acquiring method based on Hadoop. Parallel processing on statistical GPS messages is carried out through multiple MapReduce tasks, parallel calculation of the statistical congestion degree and the linearity congestion degree of each area at one time is carried out through a congestion degree calculation function, so a problem of long calculation time existing in a mobile cluster algorithm is overcome, and calculation efficiency is greatly improved. The vehicle congestion degree acquiring method based on Hadoop can simultaneously realize automatic storing of data copy and automatic expansion of a computer cluster and further has properties of high expansibility and high fault tolerance performance.

Description

technical field [0001] The invention belongs to the technical field of Internet of Vehicles communication, and more specifically, relates to a Hadoop-based method for acquiring vehicle congestion. Background technique [0002] Identifying hotspots of moving vehicles in urban areas is essential for many smart city applications, while vehicle hotspots can be described as areas with high vehicle congestion, and extremely high congestion hotspots are usually locations of traffic congestion. [0003] Intuitively, the congestion degree of a point reflects how crowded a point is. Measuring the congestion degree becomes difficult when the distribution of the vehicle set is unknown. To quantify the congestion degree of an unknown object set in a place, we propose the mobile cluster algorithm. [0004] We assume that there is a set of N vehicles deployed on a two-dimensional city plane A, among these N vehicles, a small subset is the set of sensors that we have full awareness of....

AI Technical Summary

Problems solved by technology

[0024] However, the mobile clustering algorithm is implemented in a serial manner. Firstly, the statistical GPS information is time- and regionalized through the MapReduce() function, and the GPS information at the same time t is divided into several regions, and then the calculations in each region are performed separately. After calculating the statistical congestion degree and linear congestion degree of an area, calculate the next area until all the areas at time t are calculated, and then calculate the statistical congestion degree and linear congestion degree at time t+1, press The time-growth sequence is calculated sequentially. This method uses sensor object technology to dynamically obtain information such as vehicle identification, position, instantaneous speed, forward direction, and real-time time. With the increase of traffic data acquisition sources, the amount of real-time traffic information is huge. It takes a long time and is inefficient to calculate, and it is not suitable for analyzing traffic conditions in the actual environment

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