大数据环境下的动态最短路径算法

A Dynamic Shortest Path Algorithm for Big Data

数量庞大、类型复杂的海量数据给智能交通带来了新的挑战.文中对交通诱导中的动态最短路径问题进行了研究,提出了动态交通网络数学模型,在此基础上设计了考虑交叉口延时的动态最短路径算法,并使用当前流行的大数据技术,设计了基于Ha Loop MapReduce的动态最短路径并行计算模型,最后在连续流智能交通管控平台上对算法进行了测试.实验结果表明,文中设计的算法和基于大数据的并行计算模型可以有效地查找到大规模路网中的动态最短路径,同时能很好地满足实时性需求.

Massive heterogeneous data processing has been a great challenge to intelligent traffic applications. In this paper,the dynamic shortest path problem in traffic guidance is dealt with,and a mathematic model of dynamic traffic networks is constructed. Then,a dynamic shortest path algorithm considering the intersection delay is proposed. Furthermore,a distributed and parallel processing model for solving the dynamic shortest path problem is presented based on Ha Loop MapReduce and by using big data techniques. Finally,the proposed algorithm is tested on the intelligent traffic management and control platform based on continual flow. Experimental results demonstrate that the proposed algorithm and the presented model can effectively find the dynamic shortest path in large scale networks and can meet the real-time requirement.