基于大数据的城市交通路线规划研究

Honggang Liu, F. Li, Tianran Zhang
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引用次数: 0

摘要

近年来,中国机动车数量持续增长,使道路交通拥堵问题日益严重。道路拥挤的问题已经不能靠扩建道路来解决了。大数据技术日趋成熟,为解决城市交通问题带来了新的思路。本文基于Hadoop平台,通过对路径规划算法的分析。针对现有路径规划算法的不足,对A*路径规划算法进行了改进。本文在改进A*算法路径规划的基础上得到了实时最短路径,并通过实例进行了验证,并与传统最短路径算法进行了对比分析。实验结果证明了该算法在不同交通流状态下的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Urban Traffic Route Planning Based on Big Data
In recent years, the number of motor vehicles in China has continued to grow, making road traffic congestion an increasingly serious problem. The problem of road congestion can no longer be solved by the expansion of roads. Big data technology is becoming increasingly mature, and it brings new ideas to solve the urban traffic problem. This paper is based on the Hadoop platform, through the analysis of path planning algorithms. This paper addresses the shortcomings of current path planning algorithms and improves the A* path planning algorithm. The article obtains real-time shortest paths based on the path planning of the improved A* algorithm, verifies them by example, and compares and analyses them with the traditional shortest path algorithm. The experimental results demonstrate the effectiveness of the algorithm in different traffic flow states.
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