道路交通网络节点分配优化策略研究进展

doi:10.16180/j.cnki.issn1007-7820.2023.03.013

摘要/Abstract

摘要：

为了进一步提高城市道路交通网络的通行效率,粒子群优化和神经网络等多种智能优化算法受到越来越多的关注。近年来,深度学习技术的普及与应用大幅提升了城市交通网络的节点识别效率,而交通网络的节点调度又扩展了深度学习技术的应用。文中详细分析了交通节点调度所面临的关键问题,归纳并总结了相关网络节点分配的研究现状。在此基础上,深入研讨了城市交通网络节点调度与深度学习的应用前景,并对交通网络节点分配优化策略的未来研究方向进行了展望。

关键词: 交通网络, 节点调度, 深度学习, 机器学习, 车联网, 智能算法, 启发式搜索, 协同控制

Abstract:

In order to further improve the traffic efficiency of urban road traffic network, a variety of intelligent optimization algorithms such as particle swarm optimization algorithm and neural network algorithm have attracted extensive attention. Recently, the popularization and application of deep learning technology has greatly improved the efficiency of node identification of urban traffic network, and the node scheduling of traffic network has expanded the application of deep learning technology. In this study, the key problems of traffic node scheduling are analyzed in detail, and the research status of relevant network node allocation is summarized. On this basis, the proposed study thoroughly discusses and analyzes the application prospect of node scheduling and deep learning in urban transportation network, and prospects the future research direction of node allocation optimization strategy in transportation network.

Key words: transportation network, node scheduling, deep learning, machine learning, vehicle networking, intelligent algorithm, heuristic search, collaborative control

中图分类号:

TN929.5

卢东祥. 道路交通网络节点分配优化策略研究进展[J]. 电子科技, 2023, 36(3): 81-86.

LU Dongxiang. Research Progress of Node Assignment Optimization Strategy in Road Traffic Network[J]. Electronic Science and Technology, 2023, 36(3): 81-86.

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