Spatio-temporal Analysis of the Main Types of Road Incidents in Mexico City 2018-2022

被引:0
|
作者
Hernandez-Martinez, Paola [1 ]
Islas-Cadena, Gustavo A. [1 ]
Osorio-Garcia, Noe [1 ]
机构
[1] Ctr Invest Ciencias Informac Geoespacial, Mexico City, DF, Mexico
来源
RECENT DEVELOPMENTS IN GEOSPATIAL INFORMATION SCIENCES, IGISC 2023 | 2024年
关键词
ACCIDENTS;
D O I
10.1007/978-3-031-61440-8_2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Traffic incidents have emerged as a major public health and safety concern, causing significant fatalities and injuries in the Region of the Americas. This issue is highlighted by the World Health Organization (WHO) (2018), indicating 1.35 million annual traffic-related fatalities globally, with Mexico reporting 12.9 deaths per 100,000 inhabitants. The COVID-19 pandemic has further influenced urban mobility patterns, with a surge in delivery vehicles due to increased online commerce. Despite reduced overall mobility, traffic incidents persist, necessitating a thorough assessment of their current state in Mexico City. Real-time data on traffic events is crucial for identifying high-risk areas and formulating safety measures. This study aims to construct an explanatory model for road accidents in Mexico City from 2018 to 2022, utilizing multiple regression analysis to identify spatio-temporal patterns. The findings are intended to inform policymakers on strategies to enhance road safety.
引用
收藏
页码:19 / 30
页数:12
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