[1]魏传佳 何世伟.基于大数据分析的实时交通安全监测与预警研究[J].信息化理论与实践,2018,(01):47-53.
　Research on real-time traffic safety monitoring and early warning based on big data analysis WEI Chuanjia1 HE Shiwe 2i[J].Information Theory and Practice,2018,(01):47-53.
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基于大数据分析的实时交通安全监测与预警研究()
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《信息化理论与实践》[ISSN:2520-5862/CN:]

卷:
期数:
2018年01
页码:
47-53
栏目:
出版日期:
2019-06-06

文章信息/Info

Title:
Research on real-time traffic safety monitoring and early warning based on big data analysis WEI Chuanjia1 HE Shiwe 2i
作者:
魏传佳 何世伟 2
1(泉州轻工职业学院 智能工学院 ,福建 泉州 362200)
Author(s):
1
(Department of Artificial Intelligence Management, Quanzhou College of Technology , Quanzhou 362200, China) 2(Department of Computer Management, National Taiwan University 10617 , China )
关键词:
大数据 实时 安全性 数据挖掘
Keywords:
big data real-time security data mining
摘要:
目的]目标是通过减少拥塞和碰撞风险来改进和提高城市交通系统的性能。 [方法]通过数据挖掘和贝叶斯推理技术在实时碰撞预测模型中的使用，确定影响交通运行的因素，包括间接拥塞位置和直接拥塞位置。 [结果]实验仿真结果表明共同监测、改善交通运营和安全的重要性。 [局限]对于超大规模的高速模型预测有待后续改进算法仿真其效率。 [结论]该技术能有效避免交通拥堵现象的发生，同时保证交通道路的有效利用率。
Abstract:
Objective]The goal is to improve the performance of urban transport systems by reducing the risk of congestion and collisions. [Methods]In this paper, data mining and Bayesian inference techniques are used in real-time collision prediction models to determine the factors affecting traffic operations, including indirect (peak hours, quantity and upstream low speed) congestion locations and direct congestion locations (higher downstream congestion index) ). [Results]Experimental simulation results demonstrate the importance of joint monitoring and improved traffic operations and safety. [Limitations]For ultra-large-scale high-speed model prediction, it is necessary to improve the algorithm and simulate its efficiency. [Conclusions]This technology can effectively avoid the occurrence of traffic congestion and ensure the effective utilization of traffic roads

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备注/Memo

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更新日期/Last Update: 2019-09-05