Study on Prediction Mechanism of High-speed Traffic Accidents Based on Space-Time Meteorological Grid

W. Zhang, Jun Lu, Min Zhao, Lei Liang, Ran Tang, Jiakai Peng
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Abstract

Based on the traffic accident data of the Yaxi section of the G5 Beijing-Kunming high-speed from 2018 to 2021 and the observation data of the national meteorological station corresponding to the accident occurrence point provided by the Sichuan Meteorological Office, this paper studies the prediction mechanism of the high-speed traffic accident by using the Kmeans cluster analysis, space-time grid coding and multiple logistic regression model in SPSS algorithm. In this paper, the model analysis and prediction mechanism of highway traffic meteorological disaster in western Sichuan Plateau region are studied by using data resources from meteorological and transportation departments. In this paper, a fine intelligent grid space-time model is used to improve the granularity of model analysis and prediction. Sections of meteorological elements, this article will research traffic accident data, regression analysis on the data collection using smart grid model of time and space, with meteorological elements as the emphasis, build the traffic accident of time, space, space and time on the grid data model, aiming at key points in traffic accident risk accident and the regression analysis between grid meteorological elements of time and space, Thus the traffic meteorological risk prediction model is established.
基于时空气象网格的高速交通事故预测机理研究
基于2018 - 2021年G5京昆高速雅溪段交通事故数据和四川省气象局提供的事故发生点对应的国家气象站观测数据,采用SPSS算法中的Kmeans聚类分析、时空网格编码和多元logistic回归模型对高速交通事故的预测机理进行了研究。本文利用气象和交通部门的数据资源,对川西高原地区公路交通气象灾害的模型分析和预测机理进行了研究。本文采用精细智能网格时空模型来提高模型分析和预测的粒度。在气象要素部分,本文将对交通事故数据进行研究,对数据采集采用智能网格模型进行时空回归分析,以气象要素为重点,构建交通事故的时间、空间、空间和时间网格数据模型,针对交通事故风险关键点和网格气象要素之间的时空回归分析;据此建立了交通气象风险预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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